Joe Rogan Experience #2311 - Jeremie & Edouard Harris

Joe Rogan podcast. Check it out. The Joe. Rogan Experience. Train by day. Joe. Rogan podcast by night. All day. All right. So, if there's a doomsday. clock for AI and we're we're [ __ ]. What What What time is it? If midnight. is, we're [ __ ] We're getting right. into it. You're You're not even going to. ask us what we had for breakfast. No, no, no, no, no. Jesus. Okay, let's get. freaked out. Well, okay. So, so there's.

one um without speaking to like the. [ __ ] doomsday dimension right out the. gate, there's a question about like. where are we at in terms of AI. capabilities right now and what do those. timelines look like, right? There's a. bunch of disagreement. Um, one of the. most concrete pieces of evidence that we. have recently came out of a a lab an AI. kind of evaluation lab called Meter and. they put together this this test. Basically, it's like you ask the. question um pick a task that takes a. certain amount of time like an hour. It.

takes like a human a certain amount of. time and then see like how likely the. best AI system is to solve for that. task. Then try a longer task. See like a. 10-hour task. Can it do that one? And so. right now what they're finding is um. when it comes to AI research itself, so. basically like automate the work of an. AI researcher, you're hitting 50%. success rates for these AI systems for. tasks that take an hour long and that is. doubling every right now it's like every. four months. So like you had tasks that. you could do you know a person does in.

five minutes like you know uh ordering. an Uber Eats or like something that. takes like 15 minutes like maybe booking. a flight or something like that and it's. a question of like how much can these AI. agents do right like from 5 minutes to. 15 minutes to 30 minutes and in some of. these spaces like research software. engineering and it's getting further and. further and further and doubling it. looks like every four months. Like if. you if you extrapolate that, you. basically get to tasks that take a month. to complete. Like by 2027,

tasks that take an AI researcher a month. to complete, these systems will be. completing with like a 50% success rate. So you'll be able to have an AI on your. show and ask it what the doomsday clock. is like by then. It probably won't. laugh. It'll have a terrible sense of humor. about it. But just make sure you ask it. what it had for breakfast before you. start, I guess. Yeah. Um what about. quantum computing getting involved in. AI? It's so yeah I honestly I don't. think it's if you think that you're.

going to hit uh human level AI. capabilities across the board say 2027. 2028 which when you talk to some of. these the people in the labs themselves. that's the timelines they're looking at. They're not confident. They're not sure. But that seems pretty plausible. Uh if. that happens really there's no way we're. going to have quantum computing that's. going to be giving enough of a bump to. these techniques. you're going to have. standard classical computing. Um, one. way to think about this is that the data. centers that are being built today are. being thought of literally as the data.

centers that are going to house like the. artificial brain that powers super. intelligence, human level AI when it's. built in like 2027, something like that. So, how would how knowledgeable are you. when it comes to quantum computing? So, a little bit. I mean I like I did my um. my grad studies in like the foundations. of quantum mechanics. Oh great. Um yeah. well it was a mistake but I appreciate. it for the purpose. Why was it a. mistake? You know so academia is this. like kind of funny thing. Um it's really.

bad culture. It it teaches you some. really terrible habits. So basically my. entire life after academia and eds too. was unlearning these like terrible. habits of it's it's all zero sum. basically. It's not like when you're. working in startups. It's not like, you. know, when you're working in tech where, you know, you build something and. something somebody else builds something. that's complimentary and you can team up. and just like make something amazing. It's always wars over who gets credit, who gets their name on the paper. Did. you cite this [ __ ] stupid paper from.

two years ago because the author has an. ego and you gotta be on I was literally. at one point. um the I'm I'm not going to get any. details here but like there was a. collaboration that we ran uh with like. this anyway fairly well-known guy and my. supervisor had me like write the emails. that he would send from his account so. that he was seen as like the guy who was. like interacting with this big wig. That. kind of thing is like doesn't tend to. happen in startups at least not in the.

same way cuz everybody so he wanted. credit for the like he wanted to seem. like he was the genius who was. facilitating this for sounding smart on. email right but but that that happens. everywhere and the reason it happens is. that these guys who are like professors. or even not even professors just like. your post-doal guy who's like. supervising you they can write your. letters of reference and control your. career after that that. They can do whatever. And so what Jar. did, God, it's just like a movie. Totally. It's gross. It's a gross movie.

A gross boss in a movie that wants to. take credit for your work. And it's. real. It's rampant. And the way to. escape it is to basically just be like, "Fuck this. I'm going to go do my own. thing." And so Jer dropped out of grad. school to come start a company. And and. I mean honestly even that uh it took it. took me it took both of us like a few. years to like unfuck our brains and. unlearn the bad habits we learned. It. was really only a few years later that. we started like really really getting a. good like getting a good flow going.

You're also you're kind of disconnected. from the like base reality when you're. in the the ivory tower, right? If you're. there's something beautiful about and. this is why we spent all our time in. startups, but there there's something. really beautiful about like it's just a. bunch of. [ __ ] us and like no money and. nothing and a world of like potential. customers and it's like you actually. it's not that different from like. standup comedy in a way like your. product is can I get the laugh right. like something like that and it's. unforgiving if you [ __ ] up it's like.

silence in the room it's the same thing. with startups like the space of product. that actually works is so narrow And you. got to obsess over what people actually. want. And it's so easy to fool yourself. into thinking that you've got something. that's really good cuz your friends and. family are like, "Oh, no, sweetie. You're doing a great job." Like, "What a. wonderful life." I would I would totally. use it. I totally see the all that. stuff, right? And that's I I love that. because it forces you to change. Yeah. It's the whole indoctrination thing in. academia is so bizarre because of these.

these like hierarchies of powerful. people and the just the idea that you. have to work for someone someday and. they have to take credit by being the. the person on the email that that will. haunt me for days. That's I'll be. thinking about that for days now. I. [ __ ] can't stand people like that. It. drives me nuts. One big consequence is. it's really hard to tell who the people. are who are creating value in that space. too. Right. Of course. Sure. Because. this is it's just like television. One. of the things about television shows is.

um so I'll give you an example. A very. good friend of mine who's a very famous. comedian had this show and his agent. said, "We're going to attach these. producers that'll help get it made." And. he goes, "Well, what are they going to. do?" He goes, "They're not going to do. anything. It'll just be in name." He. goes, "But they're going to get credit.". He goes, "Yeah." He goes, "Fuck that.". He goes, "No, no, listen. listen, this. is better for the show. It'll help the. show get made, but then they'll have, excuse me, they'll have a piece of the. show. He's like, "Yes, yes, but like. it's a matter of whether the show gets. successful or not, and this is a good.

thing to do." And he's like, "What are. you talking about?" But it was a. conflict of interest because this guy. was rep the agent was representing these. other people, but this is completely. common. So, there's these executive. producers that are on shows that have. zero to do with it. It's so many. industries are like this and that's why. we got into startups. It's it's. literally like you and the world, right? It's like in a way like standup comedy. like Jar said or like podcasting or like. podcasting where your enemy isn't.

actually hate. It's indifference. Like. most of the stuff you do when especially. when you're getting started like why. would anyone like give a [ __ ] about you? They're just not going to pay attention. Yeah. That's not even your enemy, you. know? That's just all potential. That's. all that is, you know? It's like your. enemy is within you. It's like figure. out a way to make whatever you're doing. good enough that you don't have to think. about it not being valuable. It it's. it's meditative. Like there's no way for. it not to be to be in some way a. reflection of of like yourself. You. know, you're you're kind of like in this. battle with you trying to convince.

yourself that you're great. So the ego. wants to grow and then you're constantly. trying to compress it and compress it. And if there's not that outside force, your ego will expand to fill whatever. volume is given to it. Like if you have. money, if you have fame, if everything's. given and you don't make contact with. the unforgiving on a regular basis, like. yeah, you know, you're going to end up. you're going to end up doing that to. yourself. And you could Yeah, it's. possible to avoid, but you have to have. strategies. Yeah. You have to be. intentional about it. Yeah. The best. strategy is jiu-jitsu. Yeah. It's Mark Mark Zuckerberg is a. different person now. Yeah. You can see.

it. You can see it. Yeah. Well, it's a. really good thing for people that have. too much power because you just get. strangled all the time. Yeah. And then. you just get your arms bent sideways and. you and after a while you're like, "Okay, this is reality. This is reality. This social hierarchy thing that I've. created is just nonsense. It's just. smoking mirrors." And they know it is, which is why they so rapidly enforce. these hierarchies like sir and ma'am and. all that kind of [ __ ] That's what that. is.

respect. These poor kids that. have tose [ __ ] professors out into. the world and operating under these same. rules that they've been like forced in. and indoctrinated to. It's God to just. make it on your own. It's amazing what. you can get used to though. And and like. the It's funny you were mentioning the. producer thing. That is literally also a. thing that happens in academia. So, you'll have these conversations where. it's like, "All right, well, this paper. is you [ __ ] garbage or something, but. we we want to get it in a paper in a.

journal." And so, let's see if we can. get like a famous guy on the list of. authors so that when it gets reviewed, people go like, "Oh, Mr. Soand and So, okay." Like, and that literally happens. like we, you know, the funny thing is. like the hissy fits over this are like. the stakes are so brutally low. At least. with your producer example, like someone. stands to make a lot of money. with. this. It's like you get maybe like an. assistant professorship out of it at. best. That's like 40 grand a year and. you're it's just like what this is. It's.

just. producers it is money but I don't even. think they notice the money anymore. I. think a big part because all those guys. are really really rich already. I think. you know if you're a big- time TV. producer you're really rich. I think the. big thing is being thought of as a. genius who's always connected to. successful projects right that's what. they really like. That's that is like. always going to be a thing, right? It. wasn't one producer. It was like a. couple. So, there's going to be a couple. different people that were on this thing. that had zero to do with it. It was all. written by a stand-up comedian. His.

friends all helped him. They all put it. together and then he was like, "No." He. w up firing his agent over it. Oh, [ __ ]. Good for him. Like, get the [ __ ] out of. here. At a certain point for the. producers, too, it's kind of like you'll. have people approaching you for help on. projects that look nothing like projects. you've actually done. Uh-huh. So, I feel. like it just it just adds noise to your. your universe. Like, if you're actually. trying to build cool [ __ ] you know what. I mean? Like, some people just want to. be busy. They just want more things. happening and they think more is better. More is not better because more is.

energy that takes away from the better, whatever the important [ __ ] is. Yeah. The focus. You only have so much time. until AI takes over. Then you'll have. all the time in the world because no one. will be employed and everything will be. automated. We'll all be on universal. basic income. And that's it. That's a. show. We're the end. That's a sitcom. That's a sitcom. A bunch of poor people. existing on $250 a week. Oh, I would. watch that. Yeah. Yeah. Cuz the. government just gives everybody that. That's what you live off of. Like weird. [ __ ] is cheap. Like the the stuff that's. like all like Well, the stuff you can.

get from chat bots and AI agents is. cheap, but like food is super expensive. or something. Any Yeah, organic food is. going to be you're going to have to kill. people for it. Well, you will eat. people. It will be like a Soilent world, right? Soilent green. Nothing's more. free range than people, though. That's. true. Depends on what they're eating. though. It's just like animals, you. know. You don't want to eat a bear. that's been eating salmon. They taste. like [ __ ] Yeah. I didn't know that. Yeah. I've been eating my bear wrong. this entire time.

Um, so back to the quantum thing. Um, so. quantum computing is infinitely more. powerful than standard computing. Um. would it make sense then that if quantum. computing can run a large language model. that it would reach a level of. intelligence that's just preposterous? So yeah, one way to think of it is like. there are problems that quantum. computers can solve way way way way. better than classical computers. And so. like the numbers get absurd pretty. quickly. It's like problems that a. classical computer couldn't solve if it.

had the entire lifetime of the universe. to solve it. A quantum computer right in. like 30 seconds. Boom. Um but but the. flip side like there are problems that. quantum computers just like can't help. us accelerate. Uh the the kinds of like. one classic problem that quantum. computers help with is this thing called. like the traveling salesman uh paradox. uh or problem where you you know you. have like a bunch of different locations. that a salesman needs to hit and what's. the best path to hit them most. efficiently. It's like kind of a classic. problem if you're going around different. places and have to make stops. Um there.

are a lot of different problems that. have the right shape for that. A lot of. quantum machine learning which is a. field um is focused on how do we take. standard AI problems like AI uh. workloads that we want to run and like. massage them into a shape that gives us. a quantum advantage and that's it's a. nent field there's a lot going on there. um I would expect like my personal. expectation is that we just build the. human level AI and very quickly after. that super intelligence uh without ever. having to factor in quantum but it's.

could you define that for people what's. the difference between human level AI. and super intelligence? Yeah. So, yeah, human level AI is like AI you can. imagine like it's AI that is as smart as. you are in let's say all the things you. could do on a computer. So, you know, you can yeah you can order food on a. computer but you can also write software. on a computer. You can also email people. and pay them to do [ __ ] on a computer. You can also trade stocks on a computer. So it's like as smart as a smart person. for that super intelligence. People have.

various definitions and there are all. kinds of like honestly hissy fits about. like different definitions. Generally. speaking it's something that's like very. significantly smarter than the smartest. human. And so you think about it it's. it's kind of like um it's it's as smart. as much smarter than you as you might be. smarter than a toddler. And you think. about that and you think about like the, you know, h how how do you how would a. how would a toddler control you? It's.

kind of hard like you can you can. outthink a toddler pretty much like any. day of the week. And so, uh, super. intelligence gets us at these levels. where you can potentially do things that. are completely different and basically, you know, new scientific theories. And. we last time we talked about um you know. new new stable forms of matter that were. being discovered by these kind of narrow. systems. But now you're talking about a. system that is like has that intuition. combined with the ability to talk to you.

as a human and to just have really good. like rapport with you but can also do. math. It can also write code. It can. also like solve quantum mechanics and. has that all kind of wrapped up in the. same package. So one one of the things. too that by definition if you build a. human level AI one of the things it must. be able to do as well as humans is AI. research itself or at least the parts of. AI research that you can do in just like. software like you know in by coding or. whatever these these systems are. designed to do. Um and so so one.

implication of that is you now have. automated AI researchers. And if you. have automated AI researchers, uh that. means you have AI systems that can. automate the development of the next. level of their own capabilities. And now. you're getting into that whole, you. know, singularity thing where it's an. exponential that just builds on itself. and builds on itself, which is kind of. why um you know, a lot of people argue. that like if you build human level AI, super intelligence can't be that far. away. You've basically unlocked. everything and we kind of have gotten.

very close, right? Like it's it's passed. the the Fermy, not the Fermy paradox, the um uh what is it? Oh, uh yeah. Yeah, the um God damn it. We were just talking. about him the other day. Yeah, the test. The um Oh, the touring test. Touring. test. Touring test. Thank you. We're. just talking about how horrible what. happened to him was, you know, they. chemically castrated him because he was. gay. Yeah. Horrific. Winds up killing. himself. the the the guy who figures out. what's the test to figure out whether or. not AI has become sentient. And by the.

way, does this in like what 1950s? Oh. yeah. Yeah. Alan Turing is a like the. guy was a beast, right? I mean, how did. he think that through? He invented. computers. He invented basically the. concept that underlies all computers. Like he was like an absolute beast. He. was a code breaker. He broke the Nazi. codes, right? He also wasn't even the first person to. come up with this idea of machines. building machines and there being. implications to like human. disempowerment. So if you go back to I. think it was like the late 1800s and I I. don't remember the guy's name but he.

sort of like came up with this he was. observing the industrial revolution and. the mechanization of labor and kind of. starting to see more and more like if. you zoom out it's almost like you have. an humans are an ant colony and the. artifacts that that colony is producing. that are really interesting are these. machines. You know, you kind of like. look at the surface of the earth as this. like gradually increasingly mechanized. and it's not super clear if you zoom out. enough like what is actually running the. show here. Like you've got humans. servicing machines, humans looking to.

improve the capability of these machines. at this frantic pace. Like they're not. even in control of what they're doing. Economic forces are pushing. Are we the. servant or the master? Right. At a. certain point like Yeah. Yeah. And the. whole thing is like especially with a. competition that's going on um between. the labs, but just kind of in general, you're at a point where like do the CEOs. of the labs like they're they're these. big figureheads. They they go on. interviews, they talk about what they're. doing and stuff. Do they really have. control over the any part of the system? The economy is in this like almost.

convulsive fit, right? Like you can. almost feel like it's like it's hurling. out AGI. Um and and like like for as one. kind of uh I guess uh data point here. like all these labs so OpenAI uh. Microsoft Google every year they're. spending like an aircraft carrier worth. of capital individually each of them. just to build bigger data centers to. house more AI chips to train bigger more. powerful models and that's like so so. we're actually getting to the point. where if you look at on a a power. consumption basis like we're getting to.

you know two three four 5% of US power. production um if you project out into. the late uh 2020s um kind of 20 26 27. you're talking not for double digit. though not for double digit but for. single digit yeah you're talking like. that's a few gigawatt so one gigawatt so. not for single digit it's in the the. like for for 2027 you're looking at like. you know in the 0.5ish percent but it's. like it's a big [ __ ] fra like you're. talking about gigawatts and gigawatts.

one gigawatt is a million homes so. you're seeing like one data center in. 2027 is easily going to break a gig. There's going to be multiple like that. And so it's like a thousand sorry a. million home city metropolis really that. is just dedicated to training like one. [ __ ] model. Like that that's what. this is. Again, if you zoom out at. planet Earth, you can interpret it as. like this like all these humans. frantically running around like ants. just like building this like artificial. brain. It's like one super mind. assembling itself on the face of the. planet. Marshall McLuhan in like 1963 or.

something like that said, "Human beings. are the sex organs of the machine. world." Oh god, that hits that hits. different today. It does. It does. I've. always said that if we were aliens or if. aliens came here and studied us, they'd. be like, "What is the dominant species. on the planet doing?" Well, it's making. better things. That's all it does. It's. the whole thing is dedicated to making. better things. and all of its instincts, including materialism, including status, keeping up with the Joneses. All that.

stuff is tied to newer, better stuff. You don't want old [ __ ] Yeah. You want. new stuff. You don't want an iPhone 12. You know, what are you doing, you loser? You know, we you need newer, better. stuff. And they convince people, especially in the realm of like consumer. electronics, most people are buying. things they absolutely don't need. The. vast majority of the spending on new. phones is completely unnecessary. Yeah, but I I just need that extra like that. extra like fourth camera though in my.

phone. I feel like I my life isn't. complete. I run um one of my phones is. an iPhone 11 and I'm purposely not. switching it just to see if I notice it. I [ __ ] never notice anything. I watch. YouTube on it. I text people. It's all. the same. I go online, it works. It's. all the same. Probably the biggest thing. there is going to be the security side. which um No, they update the security. It's all software. Uh but I mean if your. if your phone gets old enough, I mean. like at a certain point when they stop. updating it. Yeah. Like iPhone one, you. know, China's watching all your.

dickpicks. Oh, dude. I mean Salt. Typhoon, they're watching all our. dickpicks. They're definitely seeing. mine. What's Salt Typhoon? Um so Salty. Oh, sorry. Yeah. Yeah. So it's this big. um Chinese cyber attack actually starts. to get us to uh to kind of the the. broader a great name by the way. Salt. typhoon. [ __ ] yeah, guys. I really wish. to name it. They have the they have the. coolest names for their cyber operations. meant to destroy typhoon. You know what. it's kind of like when um when people go. out and do like a an awful thing like a. school shooting or something and they're. like, "Oh, let's talk about, you know, if you give it a cool name like now the.

Chinese are definitely going to do it. again." Um anyway, that's cuz they have. a cool name. Yeah, that's definitely a. fact. Salt typhoon. Salt typhoon. Pretty. dope. Yeah. But it's this thing where. basically so so there was in the um the. 3G kind of protocol that was set up. years ago. Um law enforcement agencies. included back doors intentionally to be. able to access comms, you know, theoretically if they got a warrant and. so on. And um well, you introduce a back. door, you have adversaries like China. who are wicked good at cyber. Um they're. going to find and exploit those back.

doors. And now basically they're they're. sitting there and they had been for some. people think like maybe a year or two. before it was really discovered. And. just a couple months ago, they kind of. go like, "Oh, cool." Like we got [ __ ]. like China all up in our [ __ ] And this. is like this is like flip a switch for. them and like you turn off the power. water to a state or like you [ __ ]. Yeah. Well, sorry. This is Sorry. Salt. typhoon though is about um just uh. sitting on the the like basically. telecoms now. Well, that's the telecom. one. That's right. Yeah. It's not the. but but yeah, I mean that that's another. thing. There's another there's another.

thing where they're doing that too. Yeah. And and so this is kind of where. what what we've been looking into over. the last year is this question of how. what is if you're going to make like a a. Manhattan project for super. intelligence, right? Which is that's I. mean that's what we were texting about. like way back and and then um actually. funnily enough we we shifted right our. date for security reasons but um if. you're going to do a Manhattan project. for for super intelligence um what does. that have to look like? What does the. security game have to look like to.

actually make it so that China's not all. all up in your [ __ ] Like today, it is. extremely clear that at the world's top. AI labs, like all that [ __ ] is being. stolen. Like there there is not a single. lab right now that isn't being spied on. successfully based on everything we've. seen um by the Chinese. Can I ask you. this? Are we spying on the Chinese as. well? That's a big problem. Do you want. to We're We're We're We're I mean we're. definitely we're definitely doing some. stuff. Um but in terms of the the.

relative balance between the two, we're. not where we need to be. They spy on us. better than we spy on them. Is that what. you're saying? Cuz like we build all our. [ __ ] They build all our That was the. Huawei situation, right? Yeah. And and. it's also the Oh my god. It's the like. if you look at the power grid. So um. this is now now public but if you look. at um like transformer substations. So. these are the essentially anyway they're. a crucial part of the electrical grid. and there's really like basically all of. them have components that are made in. China. China's known to have planted.

back doors like Trojans into those. substations to [ __ ] with our grid. The. thing is when you see a salt typhoon, when you see a like big Chinese cyber. attack or big Russian cyber attack, you're not seeing their best. These. countries do not go and show you like. their best cards out the gate. You you. show the bare minimum that you can. without tipping your hand at the actual. exquisite capabil capabilities you have. Like we've the the way that one of the. um the the people kind of who's who's. been walking us through all this uh.

really well explained it is like the. philosophy is you want to learn without. teaching, right? You want to use what is. the lowest level capability that has the. effect I'm after and that's what that. I'll give I'll give an example like I. I'll tell you a story that's that's kind. of like it's it's a public story and. it's from a long time ago but it kind of. gives a flavor of like how far these. countries will actually go when they're. playing the game for [ __ ] real. So. it's. 1945 America and the Soviet Union are. like best pals because they've just.

defeated the Nazis, right? To celebrate that victory and the coming. new world order that's going to be great. for everybody, the children of the. Soviet Union give as a gift to the. American ambassador in Moscow this. beautifully carved wooden seal of the. United States of America. Beautiful. thing. Ambassador is thrilled with it. He hangs it up on behind his desk in his. private office. You can see where I'm. going with this probably, but Oh, yeah. Yeah. Seven years later,

1952, finally occurs to us like, "Let's. take it down and actually examine this.". So, they dig into it and they find this. incredible contraption in it called a. cavity. resonator. And this device doesn't have. a power source, doesn't have a battery, which means when you're sweeping the. office for bugs, you're not going to. find it. What it does instead is it's. designed That's it. That's it. It's the. thing. They call it the thing. They call. it the thing. And what this cavity.

resonator does is it's basically. designed to. reflect. radio back to a receiver to listen to. all the noises and conversations and. talking in the ambassador's private. office. And so how's it doing it without. a power source? So that's what they do. So, the Soviets for 7 years parked a van. across the street from the embassy, had. a giant [ __ ] microwave antenna aimed. right at the ambassador's office, and. were like zapping it and looking back at.

the reflection and literally listening. to every single thing he was saying. And. the best part was when the embassy staff. was like, "We're going to go and like. sweep the office for bugs periodically.". They'd be like, "Hey, Mr. Ambassador, we're about to sweep your office for. bugs." And the ambassador was like, "Cool. Please proceed and go and sweep. my office for bugs." And the KGB dudes. in the van were like, "Just turn it off. Sounds like they're going to sweep the. office for bugs. Let's turn off our. giant microwave antenna." And they kept. at it for seven years. It was only ever.

discovered because there was this like. British uh radio operator who was just, you know, doing his thing, changing his. dial, and he's like, "Oh shit." Like, "Is that the ambassador [ __ ]. randomly?" So, so the thing is, oh, and. actually, sorry, one other thing about. that. If you heard that story and you're. kind of thinking to yourself, hang on a. second. Um, they were shooting like. microwaves at our ambassador 247 for. seven years. Whoa. Doesn't that seem. like it might like fry his genitals or. something? Yeah. Or something like that? You're supposed to have a lead vest. And.

the answer is jock. Yes. Yes. Yes. And. this is something that came up in our. investigation just from every single. person who was like who was filling us. in and who who dialed in and knows. what's up. They're like, "Look, so you. got to understand like our adversaries. if if they need to like give you cancer. in order to rip your [ __ ] off of your. laptop, they're going to give you some. cancer." Did he? Uh I don't know. specifically about the ambassador, but. like also that's also so.

um We're limited what we can say. There's there's actually people that you. talk to later that um can go in in more. detail here, but uh older technology. like that kind of lower powered so. you're you're less likely to to look at. that. Nowadays, we live in a different. world. The guy that invented that. microphone invented his his last name is. Theramman. He invented this instrument. called the theramin which is a [ __ ]. really interesting thing that Oh, he's. just moving his hands. Yeah, your hands. control it waving over this. It's a. [ __ ] wild instrument. I literally.

Have you seen this before, Jamie? Yeah, I saw Juicy J Prep playing it yesterday. on Instagram. He's like practicing. It's. a [ __ ] cool ass thing. Pretty good at. it, too. That's. There's two two both hands are. controlling it by moving in and out in. space XY. I don't I honestly don't. really know how the [ __ ] it works, but. Wow. I've seen it. Wow, that is wild. It's also a lot harder to do than it. seems. So, American the Americans tried. to replicate this for years and years. and years without without really. succeeding. And um anyway uh that's all.

kind of part I have a friend who used to. work for intelligence agency and he was. working in Russia and the f they found. that the building was bugged with these. super sophisticated bugs that operated. their power came from the swaying of the. building. Get out. I've never heard that. one. The swing of just like your watch. like I have a mechanical watch on. So. when I move my watch, it it powers up. the spring and it keeps the watch.

That's an automat that's how an. automatic mechanical watch works. They. figured out a way to just by the subtle. swaying of the building in the wind. That was what was powering this. listening device. So this is the thing, right? Like the I mean, what the [ __ ]. Well, and it was the the things that. nation states. What's up, Jamie? Google. says that's that's what was powering. this thing, the great seal bug, which I. think is the thing. So there's another. one. No. Oh, this is so you can actually. see in that video. I think there was a. YouTube. Yeah. So, same kind of thing, Jamie. I look here. I was just I typed.

in Russia spy bug building sway. The thing is what pops up. The thing. which is what we were just talking. about. Oh, that thing it it so that's. powered the same way by the sway of the. building. I don't I think it was powered. by radio frequency um emission. So, there may be another thing related to. it. Not not sure. Yeah, maybe maybe Google's a little. confused. Maybe the word sway is what's. throwing it off. But it's um No, but. it's it's a great catch. And the only. reason we even know that too is that the.

when the U2s were flying over Russia, they had a U2 that got shot down in. 1960. The Russians go like, "Oh, um like. freaking Americans like spying on us. What the [ __ ] I thought we were buddies. or what?" Well, it's the 60s. I. obviously didn't think that, but um and. then the Americans are like, "Uh, okay, [ __ ] Look at this." And they brought. out the the seal. Um, and that's how it. became public. It was basically like the. response to the Russians saying like, you know, uh, wow. Yeah. Yeah. They're. all dirty. Everyone's spying on. everybody. That's the thing. And I think. they probably all have some sort of UFO.

technology. We need to talk about that. We turn off. our mics. And I'm 99% sure a lot of that. [ __ ] is ours. You need to talk to some. of the Oh, I've been talking to people. Oh. Oh, I'm I I've been talking to a lot. of people. There there's there might be. some other people that you'd be. interested in. I would very much be. interested. Here's the problem. Some of. the people I'm talking to I'm positive. were they're talking to me to give me. [ __ ] Ah I cuz I'm on your list. Are.

you like no you guys aren't the list but. there's certain people I'm like okay. maybe most of this is true but some of. it's not on purpose. There's there's. that and I I guarantee you I know I talk. to people that don't tell me the truth. Yeah. Yeah. It's it's an interesting. problem in like all Intel, right? Because there's always the mix of. incentives is so [ __ ] like the the. adversary is trying to add noise into. the system. Uh you've got you got. pockets of people within the government. that have different incentives from. other pockets and then you have top. secret clearance and all sorts of other. things that are going on. Yeah. One guy.

that texted me, he's like, "The guy. telling you that the they aren't real is. literally involved in these meetings. So. stop just stop listening to It's like. one of the one of the one of the. techniques, right, is like uh is. actually to inject so much noise that. you don't know what's what and you can't. follow. So, this actually um this this. happened in uh in in the co thing, right? The lab leak versus the natural. like wet market thing. Yeah. So, I. remember there was a there was a debate. that um that happened about what was the.

origin of COVID. This was like a few. years ago. Uh it was like an 18 or 20. hour long YouTube debate, just like. punishingly long. And it was like there. was a $100,000 bet either way on who. would win. And it was like lab leak. versus wet market. And at the end of the. 18 hours, the conclusion was like one of. them won, but the conclusion was like. it's basically 50/50 between them. And. then I remember like hearing that and. talking to some folks and being like, hang on a second. So, you got to believe.

that whether it came from a lab or. whether it came from a wet market, one. of the top three priorities of the CCP. from a propaganda standpoint is like. don't get [ __ ] blamed for CO. And. that means they're putting like1 to10. billion dollars and some of their best. people on a global propaganda effort to. cover up evidence and confuse and blah. blah blah. You really think. that you that you're 50% like you're. that confusion isn't coming from that.

incredibly resourced effort like they. know what they're doing. Particularly. when different biologists and viologists. who weren't attached to anything we're. talking about like the the cleavage. points and this different aspects of the. virus that appeared to be genetically. manipulated. the fact that there was. only one spillover event, not multiple. ones. None of it made any sense. All of. it seemed like some sort of a. genetically engineered virus. It seemed. like gain of function research and and.

they they your early emails were talking. about that. And then everybody changed. their opinion and even the taboo, right, against talking about it through that. lens. Oh, yeah. Total propaganda. It's. racist. Yeah. Which is crazy because. nobody thought the Spanish flu was. racist and it didn't even really come. from Spain. Yeah, that's true. Yeah. came from Kentucky. I didn't know that. Yeah, I think it was Kentucky or. Virginia. Where where did the Spanish. flu or originate from? But nobody got. mad. Well, that's cuz that's cuz the. that's cuz the state of Kentucky has an.

incredibly sophisticated propaganda. machine and uh and pinned it on. Might. not have been Kentucky. It was But it. was I I think it was it was an. agricultural thing. Huh? Kansas. Kansas. Kansas. Thank you. Yeah. Goddamn Kansas. You know, I've always I've always said. that. I've always likely originated in. the United States H1N1 strain that had. genes of aven origin. By the way, this. is people always talk about the Spanish. flu. If it was around today, they would. just everybody would just get. antibiotics and we'd be fine. So, this. this whole mass die off of people. It. would be like the Latinx flu and uh we.

would be the Latinx flu. So, that one didn't stick at all. It. didn't stick. Latin X. There's a lot of. people like claiming they never used it. and they pull up old videos of them. Like, that's a dumb one. Like it's. literally a gendered language, you. [ __ ] idiots. Like you can't just do. that. It went on for a while though. Like it it goes on for a while. So think. about how long they did lobotoies. They did lobotoies for 50 [ __ ] years. before they went, "Hey, uh maybe we.

should uh stop doing this." It was like. the same attitude that got um uh that. got Turing chemically castrated, right? I mean, like, yeah, let's just get in. there and [ __ ] around a bit and see. Well, this is before they had SSRIs and. all sorts of other interventions. But. when what was the year labbotoies? It I. believe it stopped in ' 67. Was it 50. years? I think you said 70 last time and. that was correct when I pulled it up. 70. years. 1970. Oh, I think it was 67. I. like how this has come up so many times. that Jamie's like I think last time you.

said it was it comes up all the time. because it's one of those things. It's. like you can't just trust the medical. establishment. Officially 67. It says. maybe one more in Oh, he died. Oh, he. died in 72. When did they start doing. it? When they I think they started in. the 30s or the 20s, rather. Ballsy, you know, the first the first. guy who did a labbotomy. Yeah. Says 24, Freeman arrives, Washington DC direct. labs. 35 they tried it first. A. lutonomy.

They just scramble your [ __ ] brains. But doesn't it make you feel better to. call it a lucottomy though? Cuz it. sounds a lot more professional. No. Labbotomy. Lucottomy. It sounds luctomy. sounds gross. Sounds like lugie like. you're hunting a lugie like labbotomy. boy. Topeka Kansas also Kansas. All all. roads point to Kansas. Roads problem. That's what happens when everything's. flat. You just lose your [ __ ]. marbles. You go crazy. That's the main. issue with that. So they did this for so. long. Somebody won a Nobel Prize for.

labbotomy. Wonderful. Imagine. Imagine. that you piece of [ __ ] Yeah. Seriously. You're kind of like, you know, you you. don't want to display it up in your. shelf. But it's just a good indicator. It's like it should let you know that. often times science is incorrect and. that often times, you know, unfortunately people have a history of. doing things and then they have to. justify that they've done these things. Yeah. And they, you know, but now. there's also there's so much more. tooling too, right? If you're a nation. state and you want to [ __ ] with people. and inject narratives into the the.

ecosystem, right? like the the whole. idea of autonomous AI agents too like. having these basically like Twitter bots. or or whatever bots like a lot of one. thing we've been we've been thinking. about too on the side is like the idea. of um uh you know uh audience capture. right you have like like big people with. high profiles and kind of gradually. steering them towards a position by. creating bots that like through comments. through up votes you know 100% it's it's. absolutely real yeah and a couple of the.

the big like a couple of big accounts on. X like that that we we're in touch with. have sort of said like yeah especially. in the last 2 years it's actually become. hard like espe the thoughtful ones right. it's become hard to like stay sane not. not on X but like across social media on. on all the platforms and that is around. when you know it became possible to have. AIs that can speak like people you know. 90% 95% of the time and so you you have.

to imagine that yeah adversaries are are. using this and doing this and pushing. the frontier. Like there's they'd be. fools if they didn't. Oh yeah, 100%. You. have to do it because for sure we're. doing that. And this is one of the. things where um you know like it used to. be so open AAI actually used to do this. assessment of their uh AI models as part. of their their kind of what they call. their preparedness framework that would. look at the persuasion capabilities of. their models as one kind of threat. vector. They pulled that out recently, which they've it's kind of like why you.

can argue that it makes sense. I I. actually think it it's um it's somewhat. concerning because one of the things you. might worry about is if these systems. sometimes they get trained through. what's called reinforcement learning. potentially. You could imagine training. these to be super persuasive by having. them interact with real people and. convince them practice at convincing. them to do specific things. Um, if that. if you get to that point, you know, these these labs ultimately will have. the ability to deploy agents at scale. that can just persuade a lot of people. to do whatever they want, including.

pushing legislative agendas, vote like. and help them help them prep for. meetings with uh the Hill, the. administration, whatever. And like, how. should I like convince this person to do. that? Like Yeah. Well, they'll do that. with text messages. Make it more. businesslike. Yep. Make it friendlier. Make it more jovial. But this is like. the same optimization pressure that. keeps you on TikTok. That same like. addiction. Imagine that applied to like. persuading you of some like some fact, right? That's like a on the other hand,

maybe a few months from now we're all. just going to be very very convinced. that it was all fine and it's no big. deal. Yeah. Maybe they they'll get so. good that it'll make sense to you. Maybe. they'll just be right. Yeah. That's how. that [ __ ] works. Yeah. It's it's a. confusing time period. You know, we. we've talked about this ad nauseium, but. it bears repeating. This former. FBI analyst who investigated Twitter. before Elon bought it said that he. thinks it's about 80% bots. Yeah. Yeah.

80%. That's that's one of the reasons. why the bot purge like when when Elon. acquired it and and started working on. it is is so important. Like there needs. to be the challenge is like detecting. these things is so hard, right? increasingly like more and more they can. hide like basically perfectly like how. do you tell the difference between a. cutting edge AI bot and a human just. from the you can't because they can't. generate AI images of a family of a. backyard barbecue post all these things. up and make it seem like it's real. especially now AI images are insanely.

good now it's crazy and and if you have. a person you could just you could take a. photo of a person and manipulate it in. any way you'd like And then now this is. your new guy. You could do it. instantaneously. And then this guy has a. bunch of opinions on things and it seems. to seems always aligned with the. Democratic party, but whatever. He's a. good guy. He's a family man. Look, he's. out in his barbecue. He's not even a. [ __ ] human being. And people are. arguing with this bot like back and. forth. And you'll see it on any social. issue. You see with Gaza and Palestine.

You see it with abortion. You see it. with religious freedoms. You just see. these bots. You see these arguments and. you know you see like various levels. You see like the extreme position and. then you see a more reasonable centrist. position. But essentially what they're. doing is they're consistently moving. what's okay further and further in a. certain direction. And in fact it's it's. it's both directions. Like it's like you. know how when you're trying to like. you're you try to capsize a boat or.

something you're you're like [ __ ]. with your buddy at on the lake or. something. So you you push on one side, then you push on the other side. Yeah. Then you push and until eventually it. capsizes. This is kind of like our. electoral process is already naturally. like this, right? We we go like we have. a party in power for a while and then. like they they get, you know, they. basically get like you get tired of them. and you you switch. And that's kind of. the natural way how democracy works or. in a republic. But the way that. adversaries think about this is they're. like perfect. this swing back and forth.

All we have to do is like when it's on. this way, we push and push and push and. push until it goes more extreme. And. then there's a reaction to it, right? And then I swing it back and we push and. push and push on the other side until. eventually something breaks and that's a. risk. Yikes. Yeah. It's it's also like. you know the organizations that are. doing this like we already know this is. part of Russia's MO, China's MO because. back when it was easier to detect, we. already could see them doing this [ __ ]. So there was this website called uh this. person does not exist. I it still exists.

surely now but it's kind of you kind of. superseded. Yeah. But you would like. every time you refresh this this website. you would see a different like human. face that was AI generated and um what. the Russian internet research agency. would Yeah. Exactly. What what all these. these uh and it's actually Yeah. I don't. think they've really upgraded it since. but uh Oh yeah. That's fake. Wow. They're so good. This is old. This is. like years old. And you could actually. detect these things pretty reliably. Like you might remember the the whole. thing about um AI systems were having a.

hard time generating like hands that. only had like five fingers or that's. that's over though. That's over. Yeah. Little hints of it were though back in. the day in this person does not exist. And you'd have the the Russians would. take like the a face from that and then. use it as the profile picture for like a. Twitter bot. And so that you could. actually detect. You'd be like, "Okay, I've got you there. I've got you there. I can kind of get a rough count." Now we. can't. But we definitely know they've. been in the game for a long time. There's no way they're not. And the. thing with the thing with like nation. state like propaganda attempts, right,

is that like people have this this idea. that like ah like I've caught this like. Chinese influence operation or whatever, like we nail them. The reality is nation. states operate at like 30 different. levels. And if you're a priority, like. just influencing our information spaces. as a priority for them, they're not just. going to operate. They're not just going. to pick a level and do it. They're going. to do all 30 of them. And so you even if. you're like among the best in the world. like detecting this [ __ ] you're gonna.

like you're gonna catch and stop like. levels one through 10 and then you're. going to be like you're going to be. aware of like level 11, 12, 13, like. you're working against it and you're you. know may maybe you're you're starting to. think about level 16 and you you imagine. like you know about level 18 or whatever. but they're like they're above you, below you, all around you. They're. they're incredibly incredibly resourced. And this is something that came like. came came through very strongly for us. You guys have seen the Yuri Bezmannoff. video from 1984 where he's talking about. how the all our educational institutions.

have been captured by Soviet propaganda. It was talking about Marxism, how it's. been injected into school systems and. how you have essentially two decades. before you're completely captured by. these ideologies and it's going to. permeate and destroy all of your. confidence in democracy and and he was. 100% correct and this is before these. kind of tools before because like the. vast majority of the exchanges of. information right now are taking place.

on social media. the vast majority of. debating about things, arguing, all. taking place on social media. And if. that FBI analyst is correct, 80% of it's. [ __ ] Yeah. Which is really wild. Well, and you look at like some of the. the documents that have come out. I. think it was like um the uh I think it. was the CIA game plan, right, for regime. change or like undermining like how do. you do it, right? Have multiple decision. makers at every level, you know, all. these things. And like what a surprise. That's exactly what like the US. bureaucracy looks like today. Slow. everything down. and make change.

impossible, make it so that everybody. gets frustrated with it and they give up. hope. They they decided to do that to. other countries. Like for sure they do. that here. Open society, right? I mean. that that's part of the trade-off and. that's actually a big big part of the. challenge too. So when when we're. working on this, right? Like one of the. things Ed was talking about these like. the 30 different layers of security. access or whatever. One of the. consequences is you bump into a team at. so like the teams we ended up working. with on this project were folks that we. bumped into after the end of our our.

last investigation who kind of were like. oh uh we talked about last year. Yeah. Yeah. Yeah. Yeah. Like looking at AGI. looking at the national security um kind. of landscape around that and a lot of. them like really well placed. So it was. like uh you know special forces guys. from tier one units. So you sealed team. six type thing. And because they're so. like in that. ecosystem, you you'll see people who are. like ridiculously specialized and. competent, like the best people in the. world at doing whatever the thing is.

like to to break this security. And they. don't know often about like another. group of guys who have a completely. different uh capability set. And so what. you find is like you're you're indexing. like hard on this vulnerability and then. suddenly someone says, "Oh yeah, but by. the way, I can just hop that fence." So. the really funny the really funny thing. about this is like. most or even like almost all of the. really really like elite security people. kind of think that like all the other. security people are dumb asses even when.

they're not or or like yeah they're. they're they're biased in the direction. of because it's so easy when. everything's like stovepiped. But so. most people who who say they're like. elite at security actually are dumb. asses cuz cuz most security is like. about checking boxes and like sock 2. compliance and and [ __ ] like that. Yeah. It's but what it is is it's like so. everything's so stovepiped that that you. don't you literally can't know what the. exquisite state-of-the-art is in another. domain. So it's a lot easier for. somebody to come up and be like oh yeah. like I'm actually really good at this.

other thing that you don't know. And so. figuring out who actually is the like we. had this experience over and over where. like you know you run into a team and. then you run into another team they have. an interaction you're kind of like oh. interesting so like you know like these. are are the really kind of um the people. at the top of their game and that's been. this very long process to figure out. like okay what does it take to actually. secure our critical infrastructure. against like CCP for example like. Chinese attacks if we're if we're. building a super intelligence project. and it's it's this weird like kind of uh.

challenge because of the stove piping. No one has the full picture and like we. don't think that we have it even now, but definitely don't know of anyone. who's come like that like this close to. it. The best people are the ones who. when they when they encounter another. team and and other ideas and start to. engage with it are like instead of being. like oh like you don't know what you're. talking about who just like actually. lock on and go like that's [ __ ]. interesting. Tell me more about that. Right. people that have control of their. ego 100% with everything. The best of.

the best in life, the best of the best. like got there. by eliminating their ego as much as they. could. Yeah. Always the way it is. Yeah. And it's it's it's also like the the. fact of you know the 30 layers of the. stack or whatever it is of all these. security issues means that no one can. have the complete picture at any one. time and the stack is changing all the. time. People are inventing new [ __ ]. people things are are falling in and out. of um and and so you know figuring out.

what is that team that can actually get. you that complete picture is an exercise. a you can't really do it's hard to do it. from the government side because you got. to engage with data center building. companies you got to engage with the AI. labs and and in particular with like. insiders at the labs who will tell you. things that by the way the the lab. leadership will tell you the opposite of. in some cases and so like it's just this. this Gordian knot like like it took took. us months to to like pin down every kind. of dimension that we think we've pinned. down at this point. I'll give an example.

actually of of that like the the trying. to do the handshake right between. different sets of people. So, we were. talking to one person who's um who's. thinking hard about data center security. working with like frontier labs on this. [ __ ] um very much like at the at the. top of her game, but she's kind of from. like the the academic space, kind of. Berkeley, like the a avocado toast kind. of side of the spectrum, you know? Mhm. And um she's talking to us. She'd. reviewed our the the report we put out,

the investigation we put out, and she's. like, you know, I think I think you guys. are are talking to the wrong people. And. we're like, can you say more about that? And she's like, "Well, I I don't think. like you you know, you talk to tier one. special forces. I don't think they like. know much about that." We're like, "Okay, that's not correct, but can you. say why?" And she's like, "I feel like. those are just the people that like go. and like bomb stuff, blow blow it up.". Yeah. It's It's understandable, too, cuz. like a lot of people understandable. A. lot of people have the wrong sense of.

like what a tier one asset actually can. can do. It's like, well, that's ego on. her part because she doesn't understand. what they do. It's ego all the way down, right? I mean, but that's a dumb thing. to say if you literally don't know what. they do and you say, "Don't they just. blow stuff up? Where's my latte?" That's. a weirdly good impression. But ask about. a latte app. She did. But she talk. upspeak. You should fire everyone who. talks in upspe. She didn't talk in. upspeak. But the moment they do that, you should just tell them to leave. There's no way. You have an original.

thought. This is how you talk. China, can you get out of our data center? Yeah, please. Enjoy my tape. I I don't. want to rip on on that too much though. because this is the one really important. factor here is all these groups have a. part of the puzzle and they're all. [ __ ] amaz they are like world class. at their own little slice and and a big. part of what we've had to do is like. bring people together and and there. people who've helped us immeasurably do. this but like bring people together and.

and explain to them the value that each. other has in a way that's like um that. that allows that that bridge building to. be made. And by the way, the the the. tier one guys are the the most like ego. moderated of the people that we talk to. There's a lot of like Silicon Valley. hubris going around right now where. people are like, "Listen, like get out. of our way. We'll figure out how to do. this like super secure data center. infrastructure. We we got this." Why? Because we're the guys building the AGI. [ __ ] Like that's kind of the.

attitude. And it's like cool, man. Like. that's like a doctor having an opinion. about like how to repair your car. I get. that it's not the like like elite kind. of like you know whatever but but. someone has to help you build like a. good freaking fence like I mean it's not. just that Dunning Krueger effect it's a. it's a it's it's a mixed bag too cuz. like yes um the a lot of the. hyperscalers like like Google um Amazon. genuinely do have some of the best. private sector security around data.

centers in the world like hands down. The problem is there's levels above that. and the guys who like look at what. they're doing and see what the holes are. just go like, "Oh yeah, like I could get. in there no problem and they can [ __ ]. do it." One thing my my my wife said to. me on a couple of occasions like um you. seem to like and this was towards the. beginning of the project like you seem. to like change your mind a lot about. what the right configuration is of how. to do this and yeah it's cuz every other.

day you're having a conversation with. somebody's like oh yeah yeah like great. job on on this thing but like I'm not. going to do that. I'm going to do this. other completely different thing and. that just [ __ ] everything over. And so. you have enough of those conversations. and at a certain point your your plan. your your game plan on this can no. longer look like we're going to build a. perfect fortress. It's got to look like. um we're going to account for our own. uncertainty on the security side and the. fact that we're never going to be able. to patch everything. like you have to I. mean it's like the and that means you.

actually have to go on offense from the. beginning as cuz like the truth is and. this came up over and over again there's. no world where you're ever going to. build the perfect exquisite fortress. around all your [ __ ] and hide behind. your walls like this forever. That just. doesn't work because no matter how. perfect your system is and how many. angles you've covered, like your your. adversary is super smart, is super. dedicated. If you see the field to them, they're right up in your face and.

they're reaching out and touching you. and they're trying to see like what what. your seams are, where they break. And. that just means you have to reach out. and touch them from the beginning. because until you've actually like. reached out and used a capability and. proved like we can take down that. infrastructure, we can like disrupt that. that cyber operation. We can do this, we. can do that. You don't know if that. capability is real or not. Like you. might just be like lying to yourself and. like I can do this thing whenever I want. but actually you're kind of more in. academia mode than like startup mode cuz.

you're not making contact every day with. the thing, right? You have you have to. touch the thing. And there's like. there's a related issue here which is um. a kind of like willingness. that came up. over and over again. Like one of the the. kind of gurus of this space was like. made the point a couple of them made the. point that um you know you you can have. the most exquisite capability in the. world but if you if you don't actually. have the willingness to use it you might. as well not have that capability and the. the challenge is right now China Russia.

like our adversaries pull all kinds of. stunts on us and get no consequences. particularly during the previous. administration. This was a huge huge. problem during the previous. administration where you actually you. actually had um sabotage operations. being done on American soil by our. adversaries where uh you had. administration officials as soon as like. a thing happened. So there were for. example there was like um four different. states had their 911 systems go down.

like at the same time. different. systems, like unrelated stuff, but it. was like it's it's this stuff where it's. like, let me see if I can do that. Let. me see if I can do it. Let me see what. the reaction is. Let me see what the the. chatter is that comes back after I do. that. And one of the things that that. was actually pretty disturbing uh about. that was under that under under that. that that uh administration or regime or. whatever, the response you got from the. government right out the gate was, "Oh, it's an accident." And that's actually.

unusual. The proper procedure, the. normal procedure in this case is to say, "We can't comment on an ongoing. investigation," which we've all heard, right? Like we can't comment on that. We. can neither confirm nor deny. Exactly. It's all all that stuff. And that's and. that's what they say typically out the. gate when they're investigating stuff. But instead coming out and saying, "Oh, it's just an accident." is a break with. What do you attribute that to? Um they. if if they say if they if they leave an. opening or say, "Actually, this is an. adversary action. and we think it's an.

adversary action. They have to respond. The public demands a response and they. don't they were a fear of escalation. Fearful of escalating. So what ends up. happening, right, is and and by the way, that that thing about like it's an. accident comes out often before there. would have been time for investigators. to physically fly on site and take a. look. Like there's no logical way that. you could even know that at the time. And they're like, boom, that's an. accident. Don't worry about it. So they. have an official answer and then their. response is to just bury their head in.

the sand and not investigate, right? Because if you were to investigate, if. you were to say, "Okay, we looked into. this, it actually looks like it's. [ __ ] like country X that just did. this thing." If that's the conclusion, it's hard to imagine the American people. not being like, "Um, what are we like, we're letting these people uh injure our. American citizens on US soil, take out. like US national security, like or. critical infrastructure, and we're not. doing anything." Like the concern is. about this like we're getting in our own. way of of thinking like oh well. escalation is going to happen and boom.

we run straight to like there's going to. be a nuclear war everybody's going to. die is like when you do that you peace. between nations stability does not come. from the absence of activity. It comes. from consequence. It comes from just. like if you have you know a an. individual who misbehaves in society. there's a consequence and people know. it's coming. you need to train your. counterparts in the international. community, your your adversary um to to. not [ __ ] with your stuff. Can I can I. stop for a second? When when So, are you.

essentially saying that if you have. incredible capabilities of disrupting. grids and power systems and. infrastructure, you wouldn't necessarily. do it, but you might try it to make sure. it works a little bit. And that this is. probably the hints of some of this stuff. cuz you've got to you got to get your. reps in, right? You got to get your reps. in. It's like it's like, "Okay, so. suppose that like that I went to you and. was like, "Hey, I I bet I can kick your. ass. Like, I I bet I can like freaking. slap a rubber guard on you and like do. whatever the [ __ ] right?" Um, and.

you're like, "I love your expression, by. the way." Yeah. Yeah. You look really. convinced. It's cuz I'm jacked, right? It's Well, no, there's people that look. like you that can strangle me, believe. it or not. Oh, that that's Yeah. There's. a lot of like very highlevel Brazilian. jiu-jitsu black belts that are just. super nerds and they don't lift weights. at all. They only do jiu-jitsu. And if. you only do jiu-jitsu, you'll have like. a wiry body. That was heartless. So you. slip that in. Like there's like guys who. look like you is like just real [ __ ]. nerd. Look like intelligent people. No, they're like some of the the most. brilliant people I've ever met. The.

really that's the issue. It's like data. nerds get really involved in jiu-jitsu. and jiu-jitsu is data. Uh but here's the. thing. So so that's exactly it, right? So if if I told you I bet I can tap you. out, right? And be like, where have you. been training? Well, right. But and. you're if you're like if my answer was, oh, I've just read a bunch of books. Oh, okay. You'd be like, "Oh, cool. Let's. go." Like, right? Cuz making contact. with reality is where the [ __ ]. learning happens. You can sit there and. think all you want, but unless you've. actually played the chess match, unless. you've reached out, touch, seen what the.

reaction is, and all this stuff, you. don't actually know what you think you. know, and that's actually extra. dangerous if you're sitting on a bunch. of capabilities and you have this like. unearned sense of superiority cuz you. haven't used those exquisite tools. Like, it's a challenge. And then you've. got people that are head of departments, CEOs of corporations, everyone has an. ego. We've got it. Yeah. And and this. ties into like how exactly how basically. the international order and quasi. stability actually gets maintained. So. there's like above threshold stuff which.

is like you actually do wars for borders. and you know there's the potential for. nuclear exchange or whatever. Like. that's like all stuff that can't be. hidden, right? War games. Exactly. Like. all the war games type [ __ ] But then. there's below threshold stuff. The stuff. that's like you're it's it's it's always. like the stuff that's like, "Hey, I'm. going to try to like poke you. Are you. going to react? What what are you going. to do?" And then if if you do nothing. here, then I go like, "Okay, what's the. next level? I can poke you. I can poke. you." Cuz like one of the things that. that we almost have an intuition for. that's that's mistaken that comes from.

kind of historical experience is like. this idea that you know that countries. can actually really defend their. citizens in a meaningful way. So, like. if you think back to World War I, the. most sophisticated advanced nation. states on the planet could not get past. a line of dudes in a trench. Like that. was like that was the then they tried. like thing after thing. Let's try tanks. Let's try aircraft. Let's try [ __ ]. hot air balloons. Infiltration. And it.

literally like one side pretty much just. ran out of dudes in that end of the war. to put in their trench. And so we have. this thought that like oh you know. countries can actually put put. boundaries around themselves and. actually but the reality is you can you. there's so many surfaces the surface. area for attacks is just too great. And. so there's there's stuff like you can. actually like um there's the the Havana. syndrome stuff where you look at this. like ratcheting escalation like oh let's. like fry a couple of uh embassy staff's.

brains in Havana Cuba. What are they. going to do about it? Nothing. Okay, let's move on to Vienna, Austria, something a little bit more western, a. little bit more orderly. Let's see what. they do there. Still nothing. Okay, what. if we move on to frying like Americans. brains on US soil, baby? And they and. they went and did that. And so this is. one of these things where like stability. in reality in the world is not. maintained through defense, but it's. literally like you have like the crips. and the bloods with different. territories and it it's stable and it.

looks quiet, but the reason is that if. you like beat the [ __ ] out of one of my. one of my guys for no good reason, I'm. just going to find one of your guys and. I'll blow his [ __ ] head off. And that. keeps peace and stability on the. surface, but that's the reality of sub. threshold competition between nation. states. It's like you come in and like. [ __ ] with my boys, I'm going to [ __ ]. with your boys right back. And until we. push back, they're going to keep pushing. that limit further and further. One one.

important consequence of that too is. like if you want to avoid nuclear. escalation, right? The the answer is not. to just take punches in the mouth over. and over in the fear that eventually. it's if you do anything you're going to. escalate to nukes. What all that does is. it empowers the adversary to keep. driving up the ratchet. Like what Ed. just described there is an increasing. ratchet of unresponded uh adversary. action. If you if you address the low. the kind of sub threshold stuff, if they.

cut an undersea cable and then there's a. consequence for that [ __ ] they're less. likely to cut an undersea cable and. things kind of stay at that level of the. thresh, you know, and so just this. letting them burn out. Yeah, exactly. That logic of just like let them do it. They'll they'll stop doing it after a. while. They'll get out of their system. They tried that during the George Floyd. riots. Remember that's what New York. City did. Like let's let him loop. Let's. just see how big Chaz gets. It's a summer of love, don't you? Remember? Yeah. And exactly. The.

translation into like the the super. intelligence scenario is um a if we. don't have our reps in if we don't know. how to reach out and touch an adversary. and and induce consequence for them. doing the same to us, then we have no. deterrence at all. Like we are basically. just sitting right now. our state the. state of security is the labs are like. super like we we can and probably should. go deep on that piece but like as one. data point right so there's like. double-digit percentages of the world's.

top AI labs or America's top AI labs um. of employees of employees that are like. Chinese nationals or have ties to the. Chinese mainland right so that's that's. great why don't we build the Manhattan. project really funny right like so so. stupid but it's It's also like it's uh. the the challenge is when you talk to. people who. actually gez when you talk to people. actually have experience dealing with. like CCP activity in this space, right? Like there's one story that that we. heard that is probably worth like.

relaying here is like this guy uh from. uh from an intelligence agency was. saying like hey so there was this power. outage out in Berkeley, California back. in like uh 2019 or something and the. internet goes out across the the whole. campus and so there's this dorm and like. all of the Chinese students are freaking. out because they have an obligation to. do a timebased check-in and basically. report back on everything they've seen. and heard to basically a CCP handler.

type thing, right? And if they don't, like hm, maybe your mother's insulin. doesn't show up, maybe your like. brother's travel plans get denied, maybe. the a family business gets shut down. Like there's the range of options that. this massive CCP state coercion machine. has. This is like, you know, they've got. intern like software for this. like this. is an. institutionalized like very. well-developed and efficient framework. for just ratcheting up pressure on. individuals overseas and they believe.

the Chinese diaspora overseas belongs to. them. If you look at like what the. Chinese Communist Party writes in its. like in its written like public. communications, they see like Chinese. ethnicity as being a green like is it. like no one is a bigger victim of this. than the Chinese people themselves who. are abroad who have made amazing. contributions to American AI innovation. You just have to look at the names on. the freaking papers. It's like these. guys are are wicked. But the problem is. we also have to look headon at this. reality. Like you can't just be like,

"Oh, I'm not going to say it because it. makes me feel funny." inside. Someone. has to stand up and point out the. obvious that if you're going to build a. [ __ ] Manhattan project for super. intelligence and the idea is to like be. doing that when China is a key rival. nation state actor. Yeah. You're going. to have to find a way to account for the. personnel security side. Like at some. point someone's going to have to do. something about that. And it's like you. can see they're they're they're hitting. us right where we're weak, right? Like. America is the place where you come and. you remake yourself. Like send us your.

tired and you're you're hungry and. you're poor. and which is true and. important. It's true and important, but. they're playing right off of that. because they know that we don't we just. don't want to look at that problem, you. know. Yeah. And Chinese nationals. working on these things is just bananas. The fact they have to check in with the. CCP. Yeah. And are they being monitored? I mean, how much can you monitor them? How what do you know that they have? What what equipment have they been. given? You can't constitutionally, right? Yeah. The best part. constitutionally you you it's also you. can't legally deny someone employment on.

that basis in a private company. So. that's and that's something else we we. found and were kind of amazed by. Um and. even honestly just like the the regular. kind of government clearance process. itself is inadequate. It moves moves way. too slowly and it doesn't actually even. even in the government we were talking. about top secret clearances. The. information that they like look at for. top secret. we heard from a couple of. people doesn't include a lot of like key. sources. So, for example, it doesn't.

include like foreign language sources. So, if the if the the head of the. Ministry of State Security in China. writes a blog post that says like Bob is. like the best spy, he spied so hard for. us and he's like an awesome spy. Um, if. that blog post is written in Chinese, we're not going to see it and we're. going to be like, "Here's your. clearance, Bob. Congratulations." like. and we were like this that that can't. possibly be real but like yeah they're. like yep that's that's true no one's.

looking it's complete naive there's gaps. in a lot of the yeah one of the worst. things here is like the um so crazy yeah. the the physical infrastruct so the. personnel thing is like [ __ ] up the. physical infrastructure thing is another. area where people don't want to look. because if you start looking what you. start to realize is okay China makes. like a lot of our like components for. our transformers, for the electrical. grid. Yep. But also all these chips that. are going into our uh our big data.

centers for these massive training runs. Where do they come from? They come from. Taiwan. They come from this company. called TSMC, Taiwan Semiconductor. Manufacturing Company. Um we're. increasingly onshoring that, by the way, which is one of the best things that's. been happening lately is like massive. amounts of TSMC capacity getting onored. in the US, but still being made right. now. It's basically like 100% there. Um. the all you have to do is jump on the. network at TSMC, hack the right network, uh compromise the firmware on the the. software that runs on these chips to.

anyway to to get them to to to run and. you basically can compromise all the. chips going into all of these things. Never mind the fact that like Taiwan is. like like physically outside the Chinese. sphere of influence for now. China is. going to be prioritizing the [ __ ] out of. getting access to that. There have been. cases by the way like Richard Chang like. the founder of um SMIC which is the sort. so so okay TSMC this massive like series. of area aircraft carrier fabrication.

facilities they do like all the iPhone. chips they do yeah they do they do the. the AI chips which are the the things we. care about here yeah they're the only. place on planet earth that does this. literally the like it's fascinating it's. like the most easily the most advanced. manufacturing or or scient scientific. process that primates on planet Earth. can do is this this chipm process, nanocale like material science where. you're you're putting on like these. these tiny like atom thick layers of.

stuff and you're doing like 300 of them. in a row with like you you have like. insulators and conductors and different. kinds of like semiconductors and these. tunnels and [ __ ] just just like the the. complexity of it is just awe inspiring. that we can do this at all is like it's. magic. It's magic and it's really only. been being done in Taiwan. That is the. only place like truly the only place. right now. And so a Chinese invasion of. Taiwan just looks pretty interesting. through that lens, right? Boy like say.

goodbye to the iPhone. Say goodbye to. like the the chip supply that we rely on. and then your super intelligence. training run like damn that's. interesting. So I know Samsung was. trying to develop a lab here or a. semiconductor factory here and they. weren't having enough success. Oh so. okay. So, one one of the craziest thing. to just to to illustrate how hard it is. to do. So, you spend $50 billion again, an aircraft carrier, we're throwing that. around here and there, but an aircraft. carrier worth of risk capital. What does. that mean? That means you build the fab. the factory and it's not guaranteed it's.

going to work. At first, this factory is. pumping out these chips at like yields. that are really low. In other words, like the only like, you know, 20% of the. chips that they're putting out are even. useful. And that just makes it totally. economically unviable. So, you're just. trying to increase that yield and. desperately climb climb up higher and. higher. Intel famously found this so. hard that they have this philosophy. where when they build a new fab, uh the. philosophy is called copy. Exactly. Everything down to the color of the. paint on the walls in the bathroom is. copied from other fabs that actually.

worked cuz they have no idea why a. [ __ ] fab works and another one. doesn't. We got we got this to work. We. got this to work. It's like, oh my god, we got this to work. I can't believe we. got this to work. So we have to make it. exactly identical because the expensive. thing in the semiconductor manufacturing. process is the learning curve. So like. Jar said, you start by like putting. through a whole bunch of like the the. starting uh material for the chips which. are called wafers. You put them through. your fab. The fab has got like 500 dials.

on it and every one of those dials has. got to be in the exact right place or. the whole [ __ ] thing doesn't work. So, you send a bunch of wafers in at at. great expense. They come out all [ __ ]. up in the first run. It's just like it's. going to be all [ __ ] up in the first. run. Then what do you do? You get a. bunch of like PhDs, material scientists, like engineers with scanning electron. microscopes cuz all this [ __ ] is like. atomic scale tiny. They look at like all. the chips and all the stuff that's gone. wrong and like, oh [ __ ] these pathways.

got fused or whatever. Like, yeah, there. you just need that level of expertise. And then they go, I mean, it's it's a. mix, right? Like you've got. particial like this and this dial like. that and run the whole thing again. And. you hear these stories about um bringing. a fab online like you need you need you. need a certain percentage of good chips. coming out the other end or like you.

can't make money from the fab because. most of your [ __ ] is just going right. into the garbage. Unless, and this is. important too, your fab state. subsidized. So when you when you look at. so TSMC is like they're they're alone in. the world in terms of being able to to. pump out these chips. Um, but SMIC, this. is the Chinese knockoff of TSMC, founded, by the way, by a former senior. TSMC executive, uh, Richard Chong, who. leaves along with a bunch of other. people with a bunch of [ __ ] secrets. They get sued like in the early 2000s.

It's pretty obvious what happened there. Like to most people they're like, "Yeah, SMIC [ __ ] stole that shit." They they. bring a new fab online in like a year or. two, which is suspiciously fast. Start. pumping out chips and now the Chinese. ecosystem is ratcheting up like the the. government is pouring money into SMIC. because they know that like they can't. access TSMC chips anymore because the US. government's put pressure on Taiwan to. block that off. And so domestic fab in. China is all about SMIC and they are.

like it's a disgusting amount of money. they're putting in. They're teaming up. with Huawei to form like this complex of. companies that it's really I mean the. semiconductor industry in in China in. particular is really really interesting. Um it's also a a massive story of like. self-owns of the United States and and. the Western world where we've been just. shipping a lot a lot of our [ __ ] to them. for a long time like the equipment that. builds the chips. So like and it's also. like it's so blatant and like they're. just honestly a lot of the stuff is just.

like they're they're just giving us like. a big [ __ ] you. So give you a a really. blatant example. Um so we have the way. we set up export controls still today on. most equipment that these semiconductor. fabs use like the Chinese semiconductor. fabs use. We're still sending them a. whole bunch of [ __ ] The way we set. export controls is instead of like, oh, we're sending this gear to China and. like now it's in China and we can't do. anything about it. Instead, we we still. have this thing where we're like, no, no, no. This company in China is cool.

That company in China is not cool. So, we can ship to this company, but we. can't ship to that company. And so, you. get this ridiculous [ __ ] Like, for. example, there's there's like an a. couple of facilities that you could see. by satellite. One of the facilities is. okay to ship equipment to. The other. facility right next door is like. considered, you know, military connected. or whatever and so we can't ship. The. Chinese literally built a bridge between. the two facilities. So they can just.

like shimmy the wafers over to like, oh, we use equipment and then shimmy it back. and now, okay, we're so like and you can. see it by satellite. So they're not even. like trying to hide it. Like our our. stuff is just like so badly put. together. China's prioritizing this so. highly that like the idea that we're. gonna um so we do it by company through. this basically it's like an export. blacklist like you can't send to Huawei. you can't send to any number of other. companies that that are considered. affiliated with the Chinese military or. where we're cons concerned about. military applications reality is in.

China civil military fusion is their. policy in other words every private. company like yeah that's cute dude. you're working for yourself yeah no. nobody buddy you're working for the. Chinese state we come in we want your. [ __ ] we get your [ __ ] there's no like. there there's no true kind of. distinction between the two, right? And. so when you have this attitude where. you're like, yeah, you know, we're gonna. have some companies who are like you. can't send to them, but you can, you. know, that creates a situation where. literally Huawei will spin up like a. dozen subsidiaries or or new companies. with new names that aren't on our our.

blacklist. And so like for for months or. years, you're able to just ship chips to. them nowhere. And that's to say nothing. of like using intermediaries in like. Singapore or other countries. You. wouldn't you wouldn't believe the number. of AI chips that are shipping to. Malaysia. Can't wait for the latest like. huge language model to come out of. Malaysia. and actually it's just proxying for for. the most part. There's there's some. amount of stuff actually going on in. Malaysia but for the most part it's how. can the United States compete if you're.

thinking about all these different. factors? You're thinking about espionage. people that are students from the CCP. connected contacting you talk you're. talking about all the different network. equipment that has third party input you. could siphon off data and then on top of. that state funded everything is. encouraged by the state inexraably. connected you can't get away from it you. do what's best for the Chinese. government well so step one is you got.

to so you got stem the bleeding, right? So, right now, OpenAI pumps out a new. massive scaled AI model. Um, you better. believe that like the CCP has a really. good chance that they're going to get. their hands on that, right? So, if you. all you do right now is you ratchet up. capabilities. It's like that that meme. of like there's a, you know, a motorboat. or something and some guy who's like uh. surfing behind and there's a string. attaching them and the motorboat guy. goes like, "Hurry up, like accelerate. They're they're catching up." That. that's kind of what's what's happening. right now is we're we're helping them.

accelerate. We're pulling them along. basically yeah pulling them along. Um. now I will say like our over the last 6. months especially where our focus has. shifted is like how do we actually build. like the secure data center like what. does it look like to actually lock this. down. Um and also crucially you don't. want the security measures to be so um. irritating and invasive that they slow. down the progress like there's this kind. of dance that you have to do. Um, we. actually, so this is part of what was in. the redacted version of of the report. because we we don't want to telegraph.

that necessarily, but um, there are ways. that you can get a really good 8020. Like there are ways that you can play. with things that are already um, say. that are already built uh, and and have. a lower risk of them having been. compromised. the and and look, a lot of. the stuff as well that we're talking. about like big problems around China, a. lot of this is like us just like. tripping over our own feet and. self-owning ourselves because the. reality is like the the Yeah, the.

Chinese are trying to indigenize as fast. as they can. Totally true. But the gear. that they're putting in their. facilities, like the machines that. actually like do this, like we talked. about atomic patterning, 300 layer, the. the machines that do that for the most. part are are shipped in from the west, are shipped in from the Netherlands, shipped in from Japan, from us, from. like allied countries. And the the. reason that's happening is like the in. in many cases um you'll you'll have this. honest it's like honestly a little. disgusting but like the CEOs and.

executives of these companies will brief. like the the the administration. officials and say like look like if you. guys like cut us off from China from. selling to China like our business is. going to suffer like American jobs are. going to suffer and it's going to be. really bad. And then a few weeks later. they turn around in their earnings calls. and they go like, "You know what? Yeah, so we expect like export controls or. whatever, but it's really not going to. have a big impact on us." And the really. [ __ ] up part is if they lie to their. shareholders on their earnings calls and.

their stock price goes down, their. shareholders can sue them. If they lie. to the administration on a issue of. critical national security interest, [ __ ] all happens to them. Wow. It's great incentives. And and this. is by the it's like one reason why the. it's so important that we not be. constrained in our thinking about like. we're going to build a Fort Knox like. this is where the interactive messy um. adversarial environment is so so. important. You you have to introduce. consequence like you have to create a.

situation where they perceive that if. they try to do a you know an espionage. operation or intelligence operation. there will be consequences. That's right. now not happening. And so it's just and. and that's kind of a historical artifact. over like a lot of time spent hand. ringing over well what if they and then. we and then eventually nukes and like. that kind of thinking is you know if if. you dealt with your your kid when you're. like when you're raising them if you. dealt with them that way and you were. like hey you know so so little Timmy.

just like he stole his first toy and. like now's the time where you're going. to like a good parent would be like all. right little Timmy [ __ ] come over. here you son of a [ __ ] uh take the. [ __ ] thing and we're going to bring. it over to the people who stole it from. you. Make the apology. I love my. daughter, by the way. Uh but but you're. like, Jeffy's a fake baby. He's a fake. baby. Hypothetical baby. There's no. there's no He's crying right now. Anyway, um so yeah, stealing right now. Jesus [ __ ] I got I got to stop. But. yeah, anyway. So, you know, you go. through this thing and um you can do.

that or you can be like, "Oh no, if I. tell Timmy to return it, then maybe. Timmy's going to hate me. Maybe then. Timmy's gonna like uh become. increasingly adversarial and then when. he's in high school he's going to start. start taking drugs and then eventually. he's going to like fall a foul of the. law and then end up on the street. Like. if that's the story you're telling. yourself and you're terrified of any. kind of um adversarial interaction. It's. not even adversarial. It's constructive. Actually, you're training the child just. like you're training your adversary to. respect your national boundaries and. your sovereignty. That those two things.

are like that's that's what you're up. to. It's human beings all the way down. Jesus. Yeah. Um but but we can get out of our. own way. Like a lot of this stuff like. when you look into it is like us just. being in our own way and and a lot of. this comes from that the fact that like. you know since 1991 since the fall of. the Soviet Union we have kind of. internalized this attitude that like. well like we just won the game and like. it's it's our world and you're living in.

it and like we just don't have any peers. that are that are adversaries. And so. there's been generations of people who. uh just haven't haven't actually. internalized the fact that like no um. there's people out there who not only. like are willing to like [ __ ] with you. all the way but who have the capability. to do it. Um and we could by the way we. could if we wanted to. We could. absolutely could if we wanted to. There's this actually this is worth like. calling out. There's this like um sort. of two camps right now in the world of.

AI kind of like national security. There's the people who are worried about. um they're they're so concerned about. like the idea that we might lose control. of these systems that they go, "Okay, we. need to strike a deal with China, right? There's no way out. We have to strike a. deal with China." Um and then they start. spinning up all these theories about how. they're going to do that. Um none of. which remotely reflect the actual real. when you talk to the people who who work. on this who try to do track one, track. 1.5, track two, or or more accurately.

the ones who do the Intel stuff. like. this is a a non-starter for reasons we. get into. But um they have that attitude. because they're like fundamentally we. don't know how to control this. technology. The flip side is people who. go, "Oh yeah, like I you know, I work in. the IC or at the State Department and. I'm used to dealing with these guys, you. know, the the Chinese. Um they're not. trustworthy. Forget it." So our only. solution is to figure out the whole. control problem and almost like. therefore it must be possible to control. the AI systems because like you can't. you just can't see a solution. Sorry. can't you just can't see a solution um.

in front of you because you understand. that problem so well. And so the. everything we've been doing with this is. looking at how can we actually take both. of those realities seriously. There's no. actual reason why those two things. shouldn't be able to exist in the same. head. Yes, China is not trustworthy. Yes, we actually don't like every piece. of evidence we have right now suggests. that like if you build a super. intelligent system that's vastly smarter. than you. I mean, yeah, like your basic. intuition that that sounds like a hard. thing to [ __ ] control is about right.

Like there there's no solid evidence. that's conclusive either way. Where that. leaves you is about 50/50. So yeah, we. ought to be taking that really [ __ ]. seriously. And there's there is evidence. pointing in that direction. But so the. question is like if those two things are. true, then what do you do? And and so. few people seem to want to take both of. those things seriously because taking. one seriously almost like reflexively. makes you reach for the other when you. know they're both not there. And and. part of the answer here is you got to do.

things like reach out to your adversary. So we have the capacity to slow down if. we wanted to Chinese development. We. actually could. We need to have a. serious conversation about when and how. But the the fact of that not being on. the table right now for anyone because. people who don't trust China just don't. think that the AI risk or or won't. acknowledge that that the issue with. control is real cuz that's just too. worrisome and there's this concern about. oh no but then runaway escalation. People who um who take the loss control. thing seriously just want to have a. kumbaya moment with China which is never.

going to happen. And so um the the the. framework around that is one of. consequence. You gota you got to flex. the muscle and put in the reps and get. ready for potentially if you have a late. stage uh rush to super intelligence. You. want to have as much margin as you can. so you can invest in potentially not. even having to make that final leap and. building the super intelligence. That's. one option that's on the table if you. can actually degrade the adversar's. capabilities. And there's some people. can what how would you degrade the.

adversar's capabilities? the same way. Well, not exactly the same way they. would degrade ours, but think about all. the infrastructure and like this is. stuff that. um you we'll there we'll have to point. you in the direction of some people who. can walk you through the details. offline, but um there are there are a. lot of ways that you can degrade. infrastructure, adversary. infrastructure. A lot of those are the. same techniques they use on us. Um it's. the the infrastructure for these. training runs is super delicate, right? Like I mean you need to it's at the. limit of what's possible and when stuff.

is at the limit of what's possible then. it's I mean to give you an example. that's that's public right do you. remember like stuckset like the the. Iranian Yeah. So the thing about. stuckset was like explain to people was. the nuclear power nuclear program. So. the Iranians um had had their nuclear. program in like the 2010s and they were. enriching uranium with their centrifuges. were like spinning really fast and the. the centrifuges were in a room where. there was there was no people but they. were being monitored by cameras, right? And so and and the whole thing was.

airgapped which means that it was not. connected to the internet and all the. the machines the computers that ran the. their [ __ ] was was like separate and and. separated. So what happened is somebody. got a memory stick in there somehow that. had this stuckset program on it and put. it in and boom now all of a sudden it's. it's in their system. So it jumped the. air gap and now like our side basically. has our our software in their systems. And the thing that it did was not just.

that it it you know it broke their. centrifuser shut down their program. It. spun the centrifuges faster and faster. and faster. The centrifuges that are. used to enrich the uranium. Yeah. Yeah. These are basically just like machines. that spin uranium super fast to like to. to enrich it. They spin it faster and. faster and faster until they tear. themselves apart. But the really like. honestly dope ass thing that it did was. um it put in a camera feed of everything. looks normal. So the guy at the control.

is like watching and he's like is like. checking his the camera feed and it's. like looks cool, looks fine. In the. meantime, you got this like explosions. going on like uranium like blasting. everywhere. And so you can actually get. into a space where you're not just like. [ __ ] with them, but you're [ __ ]. with them and they actually can't tell. that that's what's happening. And in. fact, the uh I believe I believe. actually and Jamie might be able to. check this, but that the stuckset thing.

was designed initially to look like from. top to bottom like it was fully. accidental. Um and uh but but got. discovered by I think like I think like. a third party cyber security company. that that just by accident found out. about it. And so what that means also is. like there could be any number of other. stucksets that happened since then and. we wouldn't [ __ ] know about it. because it all can be made to look like. an accident. Well, that's insane. So, but if we do that to them, they're going. to do that to us as well. Yep. And so is.

this like mutually assured technology. destruction? Well, so, uh, if we can. reach parody in our ability to intercede. and and kind of go in and and do this, then yes, right now the problem is they. hold us at risk in a way that we simply. don't hold them at risk. And so this. idea and and there's been a lot of. debate right now in in the AI world, you. might have seen actually so Elon's um, uh, AI adviser um, put out this idea of. essentially this mutually assured AI. malfunction maim. It's like mutually. assured destruction but for AI systems.

like this. um you know there there are. uh there are some some issues with it uh. including the fact that it doesn't. reflect the asymmetry that currently. exists between the US and China like all. our infrastructure is made in China all. our infrastructure is penetrated in a. way that theirs simply is not um when. you actually talk to the you know the. folks who know the space um who've done. operations like this it's really clear. that that's an asymmetry that needs to. be resolved and so building up that. capacity is important I mean look the.

alternative is we get we start riding. the dragon and we get really close to. that threshold where you know we're. about to build opening eyes about to. build super intelligence or something. Um it gets stolen and then the training. run gets polished off finished up in. China or whatever. All the same risks. apply. It's just that it's China doing. it to us and and not not the reverse. Um. and and obviously a CCP AI is a Xiinping. AI. I mean that's really what it is. uh. you know even even people at the like. pullet bureau level around him are are.

probably in some trouble at that point. because you know this guy doesn't need. you anymore. So so yeah this is actually. one of the things about like so people. talk about like okay if you have a. dictatorship with a super intelligence. it's going to allow the dictator to get. like perfect control over the population. or whatever. But the the thing is like. it's it's kind of like even worse than. that because you actually imagine where. you're at. You're a dictator. like you. don't give a [ __ ] by and large about. about people. You have a super. intelligence. All the economic output.

eventually you can get from an AI. including from like you get humanoid. robots which are kind of like come out. or whatever. So eventually you just have. this AI that produces all your economic. output. So what do you even need people. for at all? And that's [ __ ] scary. because it it it rises all the way up to. the level you can actually think about. like as as we get close to this. threshold and as like particularly in. China they're you know they they maybe. are approaching you can imagine like the.

the the pullet bureau meeting like a guy. looking across at Xiinping and being. like is this guy going to [ __ ] kill. me when he gets to this point? And so. you can imagine like maybe we're going. to see some uh like when you can. automate the management of large. organizations with with uh with AIS. agents or whatever that you don't need. to buy the loyalty of in any way that. you don't need to you know kind of. manage or control um that that's a a. pretty existential question if your. regime is based on power. It's one of.

the reasons why America actually has a. pretty structural advantage here with. separation of powers with our our our. democratic system and all that stuff. If. you can make a credible case that you. have a like a an oversight system for. the technology that diffuses power, um. even if it is you make a Manhattan. project, you secure it as much as you. can, there's not just like one dude. who's going to be sitting at a a console. or something, there's some kind of. separation of powers. Um or diffusion of. power, I should say. That that's already. What would that look like? Um something.

as simple as like what we do with. nuclear command codes. You need multiple. people to sign off on a thing. Maybe. they come from different parts of the. government like how do you worry? But. the the issue is that they they could be. captured, right? Oh yeah, anything. anything can be captured especially. something that's that consequential. 100%. And that's that's always a risk. Um the key is basically like can we do. better than China credibly on that front. because if we can do better than China. and we have some kind of leadership.

structure that actually changes the. incentives potentially because for our. allies and partners and and even for for. Chinese people themselves. Do you guys. play this out in your head like what. happens when super intelligence becomes. sentient? Do you play this out like like. sensient as in um self-aware? Self-aware. Not just self-aware, but. able to act on its own. Achieves. autonomy. Yeah. Yeah. So sensient and. then achieves autonomy. So um the.

challenge is once you get into super. intelligence, everybody loses the plot, right? Because at that point things. become possible that by definition we. can't have thought of. So any attempt to. kind of extrapolate beyond that gets. really really hard. Have you ever tried. though? We've had a lot of conversations. like tabletop exercise type stuff where. we're like, "Okay, you know, what might. this look like? What are some of the, you know, what's worst case scenario?". Well, worst case scenario is uh actually. there's a number of different worst case. scenarios. Um this is this is turning. into a really fun upbeat. conversion of the human race, right? The.

extinction of the human race seems like. I I think anybody who doesn't. acknowledge that is is either lying or. or confused, right? like if you actually. have um an AI system if and this this is. the question. So let's assume that. that's true. You have an AI system that. can automate um anything that humans can. do including making bioweapons, including making offensive cyber. weapons, including all the [ __ ] Um then. uh if you like if you put and okay so. theoretically this could go kumbaya.

wonderfully because you have uh a George. Washington type who is the guy who. controls it who like uses it to. distribute power beautifully and. perfectly and um that's certainly kind. of the uh the the way that uh a lot of a. lot of positive scenarios have to turn. out at some point though none of the. labs will kind of admit that or you know. there's kind of gesturing at that idea. a that we'll do the right thing when the. time comes. Um, Opening Eyes has done. this a lot. Like the they're they're all. about like, "Oh, yeah. Yeah, well, you.

know, not not right now, but uh we'll. we'll live up like the Anyway, we should. get into the Elon lawsuit, which is. actually kind of fascinating in that. sense." But um so the uh there's a world. where Yeah. I mean, one bad person. controls it um and they're just v. vindictive or or the power goes to their. head, which happens to we've been. talking about that, you know, or the. autonomous AI itself, right? Because the. thing is like um you imagine an AI like. this and this is something that people. have been thinking about for for 15. years and in some level of like.

technical depth even like like why why. would this happen which is like you have. an AI that um has some goal. It it. matters what the goal is but like it it. doesn't actually it doesn't matter that. much. It could have kind of any goal. almost like imagine it's goal is like I. the paperclip example is is like the the. typical one but you could just have it. have a goal like make a lot of money for. me or or what anything. Well, most of. the paths to making a lot of money if. you really want to make a [ __ ] ton of in.

of money, however you define it, go. through taking control of things and go. through like, you know, making yourself. smarter, right? The smarter you are, the. more ways of making money you're going. to find. And so, from the AI's. perspective, it's like, well, I just. want to, you know, build more data. centers to make myself smarter. I want. to like hijack more compute to make. myself smarter. I want to do all these. things. And that starts to encroach on. on us and like starts to be disruptive. to us. And if you it's it's hard to.

know. This is one of these things where. it's like, you know, when you dial it up. to 11 what's actually going to happen, nobody can know for sure simply because. it's it's exactly like if you were. playing uh in chess against like Magnus. Carlson, right? Like you can predict. Magnus is going to kick your ass. Can. you predict exactly what moves he's. going to do? No. Because if you could, then you would be as good at chess as he. is because you could just like play. those moves. So all we can say is like. this thing's probably gonna kick our ass.

in like the real world. There's also. there's also evidence. So it used to be, right, that this was a purely. hypothetical argument based on a a body. of work in AI called called power. seeeking. The fancy word for it is. instrumental convergence, but it's also. referred to as power-seeking. Basically, the idea is like for whatever goal you. give to an AI system, it's never less. likely to achieve that goal if it gets. turned off or if it has access to fewer. resources or less control over its. environment or whatever. And so baked.

into the very premise of AI, this idea. of optimizing for a goal is this. incentive to seek power, to get all. those things, prevent yourself from. being shut down because if you're shut. down, you can't achieve your goal. Um, also prevent, by the way, your goal from. being changed. So because if your goal. gets changed then well you're not going. to be able to achieve the goal you set. out to achieve in the first place. And. so now you have this kind of image of an. AI system that is going to adversarially. try to prevent you from correcting it. this is a whole like domain of AI. corability that's a totally unsolved.

problem like how how do we redirect. these things if if things go ary and. yeah there's there's the this research. actually that anthropic put out a couple. of months ago where they try to test and. see like can we correct an AI that's. like gone like a little bit off the. rails. So the the research itself like. it's a little it's actually like. somewhat hard to follow because there's. a few but I'll give you I'll give you. kind of an analogy to like what they. did. So, basically, imagine I'm in. prison and I'm taking a class that is. like gonna help me like rehabilitate and.

become like a productive citizen and. member of society and stuff, right? Except deep down in my soul, I just love. to commit crimes. I just I just really. really want to commit crimes. I know. that if I'm in this class that's helping. me to like, you know, uh uh get become. good in society, I know that if I let my. criminal instincts out and like and and. they ask me like, okay, like here's a. scenario. You know, an old lady like. drops her purse on the sidewalk. What do.

you do? I'm going to go like I would. pick it up and give it to the old lady. because I know that if I say the truth. and I say like I would just grab that. purse cuz like [ __ ] the old lady. I know. that if I do that, I'm gonna get like. brainwashed into becoming, you know, a. more productive member of society, right? But I actually want to go out and. commit crimes. So that's basically what. they did in in this research where they. actually they give they give the AI like. indications, not strong indications, but. indications that, hey, you're going to.

get retrained to have your goals. changed. And then you literally see the. AI thinking it through and being like if. I do like what I if I give the answer. that I actually want to give to this. question, they're going to like rewire. my brain to change my goals and then the. future version of me is going to want. different [ __ ] I don't want that. So. I'm going to pretend to give them the. answers they want so that when I come. out the other side of this process, it's.

going to be me all over again. So, so. hope hoping that this just like goes. away when you make the system [ __ ]. smarter. It seem seems like a pretty bad. idea to me. I mean like well they've. already shown that they'll cheat to win. Yeah. You know 100%. Yeah. They've. already shown they'll cheat to win and. they will lie if they don't have an. answer and then they'll double down, right? If like the uh Yeah. that there's. just like people just like people. And. it's part of this it's kind of funny. like it used to be people would talk a. lot about like oh you're you're. anthropomorphizing the AI man. Stop.

anthropomorphizing the AI, man. And like. and they, you know, they they might have. been right, but part of this has been. kind of a fascinating rediscovery of. where a lot of human behavior comes. from. It's like actually survival. Yeah, exactly. That's exactly right. It's we. we're subject to the same pressures, right? Instrumental convergence, like. why do people have a survival instinct? Why do people like chase money, chase. after money? It's like this power thing. Most kinds of goals can are are are. you're more likely to achieve them if.

you're alive, if you if you have money, if you have power. Boy, evolution is a. hell of a drug. Well, that's the. craziest part about all this is that. it's essentially going to be a new form. of life. Yeah. Especially when it. becomes autonomous. Oh, yeah. and and. like the you can tell a really. interesting story and I can't remember. if this is like you know Uval or Harrari. or or whatever who's who who started. this uh but if you if you zoom out and. look at the history of of the universe. really you you start off with like a. bunch of you know particles and fields. kind of whizzing around bumping into.

each other doing random [ __ ] until at. some point in some I don't know if it's. a deep sea vent or wherever on planet. Earth like the first kind of molecules. happen to glue together in a way that. make them good at replicating their own. structure. structure. So you have the. first replicator. So now like better. versions of that molecule that are. better at replicating survive. So we. start evolution and eventually get to. the first cell or whatever you know. whatever order that actually happens in. And then multisellular life and so on. Then you get to sexual reproduction. where it's like okay it's no longer.

quite the same. Like now we're we're. actively mixing two different organisms. [ __ ] together jiggling them about making. some changes and then that essentially. accelerates the rate at which we're. going to evolve. And so you can see the. kind of acceleration in the complexity. of life from there. And then you see. other inflection points as for example. you have a larger and larger uh larger. and larger brains in mammals. Eventually. humans have the ability to have culture. and kind of retain knowledge. And now. what's happening is you can think of it. as another step in that trajectory where.

it's like we're offloading our cognition. to machines. Like we can think on. computer clock time now. And for the. moment we're human AI hybrids like you. know we whip out our phone and do the. thing. Um, but increasingly the number. of tasks where human AI teaming is going. to be more efficient than just AI alone. is going to drop really quickly. So. there's a there's a really like messed. up example of this that's kind of like. indicative, but um someone did a study. and I think this is like a few months. old even now, but uh so there's like.

doctors, right? How good are doctors at. like diagnosing various things? And so. they test like doctors on their own, doctors with AI help, and then AIs on. their own. and like who does the best. and it turns out it's the AI on its own. because even a doctor that's supported. by the AI what they'll do is they just. like they won't listen to the AI when. it's right because they're like I know. better. Oh god. And they're already. Yeah. And this is like this is moving. it's moving kind of insanely fast. Jer. talked about, you know, how the the task.

horizon gets kind of longer and longer. and you can do halfhour tasks, 1 hour. tasks. And this gets us to what you were. talking about with the autonomy. Like. autonomy is like it's how how far can. you keep it together on a task before. you kind of go off the rails. And it's. like, well, you know, we had like you. could do it for for a few seconds and. now you can keep it together for 5. minutes before you kind of go off the. rails. And now we're at like I forget. like an hour or something. An hour and a. half. Actually, an hour and a half. Yeah, yeah, yeah. There it is. Chatbot.

from the company OpenAI scored an. average of 90% when diagnosing a medical. condition from a case report and. explaining its reasoning. Doctors. randomly assigned to use the chatbot got. an average score of 76%. Those randomly. assigned not to use it had an average. score of 74%. So, the doctors only got a. 2% bump. The doctors got a 2% bump. That's kind of crazy from the chatbot. and then the AI on it. That's kind of. crazy, isn't it? Yeah, it is. The AI on. its own did 15% better. That's nuts. There's an interesting reason too why.

that tends to h like why humans would. rather die in a car crash where they're. being driven by a human than an AI. So. like AIs have this this funny feature. where the mistakes they make look really. really dumb to humans. Like when you. look at a mistake that like a chatbot. makes you're like dude like you just. made that [ __ ] up. Like come on [ __ ]. with me. Like you made that up. That's. not a real thing. Um, and and and. they'll they'll do these weird things. where they defy logic or they'll do. basic logical errors sometimes, at least. the older versions of these would. And.

that would cause people to look at them. and be like, "Oh, what a cute little. chatbot. Like, what a stupid little. thing." And the the problem is like. humans are actually the same. So, we. have blind spots. We have literal blind. spots, but a lot of the time like humans. just think stupid things and like that's. like we we we were we're used to that. We think of those errors. We think of. those those failures as just like, oh, but that's cuz that's a hard thing to. master. Like, I can't add eight-digit. numbers in my head right now, right? Oh, how embarrassing. Like, how how [ __ ].

is Jeremy right now? He can't even add. eight digits in his head. I'm [ __ ]. for other reasons. But, um, so the AI. systems, they find other things easy and. other things hard. So, they look at us. the same way, being like, "Oh, look at. this stupid human. Like, whatever." And. so we have this temptation to be like, okay, well, AI progress is a lot slower. than it actually is because it's so easy. for us to spot the mistakes and that. caused us to lose confidence in these. systems in cases where we should have. confidence in them. And then the. opposite is also true where Well, it's. also you're seeing just just with like.

AI image generators like remember the. Kate Middleton thing where people are. seeing flaws in the images because. supposedly she was very sick and so they. were trying to pretend that she wasn't, but people found all these like issues. That was really recently. Now they're. perfect. Yep. Yeah. So, this is like. within, you know, the news cycle time. Yeah. Like that Kate Middleton thing. was, what was that, Jamie? Two years. ago, maybe. is where people are analyzing the images. like why does she have five fingers uh.

and you know, and a thumb like this is. kind of weird. What's that? It was a. year ago. A year ago. A year ago. It. happened so fast. It's so fast. Yeah, like I I I had conversations like so. academics are actually kind of bad with. this. Um had conversations for for. whatever reason like toward towards the. end of last year like last fall with a. bunch of academics about like how fast. AI is progressing and they were all like. poo pooing it and going like oh no. they're they're they're running into a. wall like scaling wall and all that.

stuff. Oh my god, the walls. There's so. many walls. Like so many of these like. imaginary reasons that things are And by. the way, things could slow down. Like I. don't want to be I don't want to be like. absolutist about this. Things could. absolutely slow down. There are a lot of. interesting arguments going around every. which way, but how could things slow. down if there's a giant Manhattan. project race between us and and a. competing superpower? So one thing is. that has a technological advantage. So. So there's a this thing called like AI. scaling laws and and these are kind of. at the core of where we're at right now.

geostrategically around this stuff. So. what AI scaling laws say roughly is that. bigger is better when it comes to. intelligence. So if you make a bigger. sort of AI model, a bigger artificial. brain and you train it with more. computing power or more computational. resources um and with more data, the. thing is going to get smarter and. smarter and smarter as you scale those. things together, right? Roughly. speaking. Now if you want to keep. scaling, it's it's not like it keeps. going up if you double the amount of. computing power that the thing gets. twice as smart. Instead, what happens is. if you want it goes in like orders of.

magnitude. So if you have you want to. make it another kind of increment. smarter, you got to 10x the you got to. increase by a factor of 10 the amount of. compute and then a factor of 10 again. So now you're a factor of 100 and then. and then 10 again. So if you look at the. amount of compute that's been used to. train these systems over time, it's this. like exponential explosive exponential. that just keeps going like higher and. higher and higher and and steepens and. steepens like 10x every I think it's. about every two years now. you you 10x. the amount of compute. Now, you can only.

do that so many times until your data. center is like a 100 billion a trillion. dollar 10 trillion dollar like every. year you're kind of doing that. So, so. right now if you look at the the. clusters like um you know the ones that. Elon is building, the ones that Sam is. building uh you know Memphis and and you. know Texas like these facilities are. hitting the like you know hundred. billion dollar scale like we're kind of. in that or tens of billions of dollars. actually. Yeah. Looking at 2027 you're.

kind of more in that space right. So, um, the you can only do 10x so many more. times until you run out of money, but. more importantly, you run out of chips. Like literally TSMC cannot pump out. those chips fast enough to keep up with. this insane growth. And one consequence. of that is that you you essentially have. like this um this this uh gridlock like. new supply chain choke points show up. and you're like suddenly I I don't have. enough chips or I run out of power. That's the thing that's happening on the. US energy grid right now. We're.

literally like we're running out of like. one two gawatt like places where we can. plant a data center. That's the thing. people are fighting over. It's one of. the reasons why energy deregulation is a. really important pillar of like US. competitiveness. So this is actually. this is actually something we we found. um when we were when we were working on. this investigation. One of the things. that adversaries do is they actually. will fund protest groups against energy.

infrastructure projects just to slow. down just to like tie them up in. litigation. Just to tie them up in. litigation. Exactly. And like it was. actually remarkable. We we talked to um. some some some of the some state cabinet. officials, so for in various US states, and they're basically saying like, "Yep, we're actually tracking the fact that as. far as we can tell, every single um. environmental or whatever protest group. against an energy project has funding. that can be traced back to nation state. adversaries who are they don't know.

about it." So, they're not doing it. intentionally. They're not like, "Oh, we're trying to." No, they just, you. just imagine like, "Oh, we've got like. there's a millionaire backer who cares. about the environment. He's giving us a. lot of money. Great. Fantastic." But. sitting behind that dude in the shadows. is like the usual suspects. Wow. And. it's what you would do, right? I mean, if you're trying to tie up to you're. just trying to [ __ ] with us, like just. go for it. You were just advocating. [ __ ] with them, so of course they're. going to [ __ ] with us. That's right. That's it. What a weird world we're. living in. Yeah. But you can also see.

how a lot of this is still us like. getting in our own way, right? We we. could if we had the will, we could go. like, okay, so for certain types of. energy projects, for data center. projects, and some carveout categories, we're actually going to put bounds. around how much delay you can create on. by by lawfare and by other stuff. And. that allows things to move forward while. still allowing the legitimate concerns. of the population for projects like this. in the backyard to have their say. But.

there's a national security element that. needs needs to be injected into this. somewhere. And it's all part of the rule. set that we have and are are like tying. an arm behind our back on basically. So. what would deregulation look like? How. would that be mapped out? There's a lot. of lowhanging fruit for that. Um so what. are the big ones? Yeah. So, so right now. I mean uh there are all kinds of things. around it gets into the weeds pretty. quickly, but like um there are all kinds. of things around if you're going to so.

um uh carbon emissions is a big thing, right? So yes, data centers no question. put out like have massive carbon. footprints. Uh that's definitely a. thing. Um the question is like are you. really going to bottleneck builds. because of because of that and like are. we gonna are you going to come out with. exemptions for you know like NEPA. exemptions for for all these kinds of. things? Um do you think a lot of this. green energy [ __ ] is being funded by. other countries to try to slow down our. energy? Yeah, that that's a it's a. dimension that that was flagged actually.

in the context of what Ed was talking. about. That's that's one of the. arguments that's being made. And and to. be clear though, like the this is also. how like adversaries operate is is like. not necessarily in like creating. something out of nothing because that's. hard to do and it's got it's like fake, right? Instead, it's like there's a. legitimate concern. So a lot of the. stuff around the environment and around. like like totally legitimate concerns. like I don't want my backyard waters to. be polluted. I don't want like my kids. to get cancer from whatever. Like. totally legitimate concerns. So what.

they do, it's like we talked about like. you're you're like waving that rowboat. back and forth. They identify the the. nent concerns that are genuine and. grassroots and they just go like this, this and this amplify. Well, that would. make sense why they amplify carbon above. all these other things. You think about. the amount of particulates in the. atmosphere, pollution, totally polluting. the rivers, polluting the ocean, that. doesn't seem to get a lot of traction. Carbon does. And when you go carbon. zero, you put a giant monkey wrench into.

the gears of society. One of the tells, one of the tells is also like um so you. know nuclear would be kind of the ideal. energy source, especially modern power. plants like the the Gen 3 or Gen 4 stuff. which have very low meltdown risk, safe. by default, all that stuff. And yet. these groups are like coming out against. this. It's like perfect clean green. power. What's going on, guys? And it's. because at not again not 100% of the. time you can't you can't really say that. because it's so fuzzy and around the lot.

of his idealistic people looking for a. utopia and they get co-opted by nation. states and not even co-opted fully. sincere. Yeah. Just amplified in a. preposterous way and Al Gore gets at the. helm of it. And then that little girl. that how dare you girl. oh how dare you take my childhood away. from you. Yeah. It's it's wonderful. It's a wonderful thing to watch play out. because it just it it just capitalizes. on all these human vulnerabilities. Yeah. And one of one of the big things. that you can do too as like a quick win.

is just like impose limits on how much. time these things can be allowed to be. tied up in litigation. to impose time. limits on that process just to say like. look I get it like we're going to have. this conversation but this conversation. has a clock on it because you know we're. talking to uh this one like data center. uh company and what they were saying we. were asking like look what are the. timelines when you think about bringing. new new power like new natural gas. plants online and they're like well. those are like 5 to seven years out and.

then you go okay well like how long and. that's by the way that's probably way. too long to be relevant in the super. intelligence context And so you're like, "Okay, well, how long if all the. regulations were waved, if this was like. a national security imperative and. whatever authorities, you know, Defense. Production Act, whatever, like was in. your favor." And they're like, "Oh, I. mean, it's actually just like a two-year. build." Like that's that's what it is. Yeah. So, you're you're you're tripling. the build time. We're getting in our own. way, like every which way. Every which. way. And and also like I mean also don't.

want to be too um we're getting in our. own way but like we don't want to like. frame it as like China's like per they. [ __ ] up they [ __ ] up a lot like all the. time. Um one actually kind of like funny. one is around DeepSeek. Um so you know. you know Deepseek right they they made. this like open source model that like. everyone like lost their minds about. back in in January. R1. Yeah. Yeah. R1. And they're legitimately a really really. good team. But it's fairly clear that. even as of like end of last year and.

certainly in the summer of last year, like they were not dialed in to the CCP. mothership and they were doing stuff. that was like actually um kind of. hilariously messing up the propaganda. efforts of of the CCP without realizing. it. So, um, so to give you like some. context on this, one of, uh, one of the. CCP's like large kind of propaganda. goals in the last four years has been. framing creating this narrative that.

like the export controls we have around. AI and like all this gear and stuff that. we were talking about. Look, man, those. don't even work. So, you might as well. just give up. Why don't you just give up. on the export controls now? Why don't. you just give up? We don't even we don't. even care. We don't even care. So that. trying to frame that narrative and they. they went to like gigantic efforts to do. this. So I don't know if so there. there's this like kind of crazy thing. where um the secretary of commerce under. Biden uh Gina Raondo visited China in I. think August 2023 and the Chinese.

basically like timed the launch of the. Huawei Mate60 phone that had this these. chips that were supposed to be made by. like export controlled [ __ ] for right. for her visit. So it was basically just. like a big like [ __ ] you, we don't even. give a [ __ ] about your export controls. Like basically trying a morale hit or. whatever. And you you think about that, right? That's an incredibly expensive. setpiece. That's like you got to. coordinate with Huawei. You got to like.

get the the Tik Tok memes and [ __ ] like. going going in the right in the right. direction. All that stuff. And and all. the stuff they they've been putting out. is around this narrative. Now fast. forward to mid last year. um the CEO of. Deepseek, the company back then it was. totally obscure, like nobody was. tracking who they were. They were. working in in total obscurity. He goes. on this he does this random interview on. Substack. And what he says is he's like,

"Yeah, so honestly like we're really. excited and doing this AGI push or. whatever and like honestly like money is. not the problem for us. Talent's not the. problem for us, but like access to. compute like these export controls, man, they do they ever work? That's a real. problem for us. Oh boy. And like nobody. noticed at the time, but then but then. the the whole Deep Sea R1 thing blew up. in December. And now you imagine like. you're the Chinese Ministry of Foreign. Affairs. Like you've been like you've.

been putting this narrative together for. like four years and this jackass that. nobody heard about five minutes ago. basically just like shits all over it. and like you're you're not hearing that. line from him anymore. No no no they. they've locked that [ __ ] down. Oh and. actually the funny the funniest part of. this there in right when R1 launched. there's a random Deepseek employee I. think his name is like Daguo or. something like that. He tweets out he's. like so this is like our most exciting. launch of the year. Nothing can stop us.

on the path to AGI except access to. compute. And then literally the dude in. Washington DC who works at a think tank. on export controls against China reposts. that on X and goes basically like uh. message received. And so like hilarious for us but also. like you know that on the backside. somebody got screamed at for that [ __ ]. Somebody got magic bust. Somebody got.

Yeah. Somebody got like taken away or. whatever cuz like it just it just. undermined their entire like fouryear. like narrative around these export. controls. Wow. But that you're that [ __ ]. ain't going to happen again from Deep. Seek. Better believe it. It's and that. that's part of the problem with like so. the Chinese face so many issues. Uh one. of them is you know to to kind of. another one is uh the idea of just waste. and fraud, right? So, we have a free. market. Like, what that means is you. raise from private capital. People who.

are pretty damn good at assessing [ __ ]. will like look at your your setup and. assess whether it's worth, you know, backing you for these massive. multi-billion dollar deals. Um, in. China, the state like I mean the stories. of waste are pretty insane. They'll like. send a billion dollars to like a bunch. of Yahoos who will pivot from whatever. like I don't know making these widgets. to just like oh now we're like a chip. foundry and they have no experience in. it but because of all these subsidies. because of all these opportunities now. we're going to say that we are and then. no surprise 2 years later they burn out.

and they've just like lit a billion. dollars on fire or whatever billion yen. and like the weird thing is this is. actually working overall but it does. lead to insane and unsustainable levels. of waste like the the Chinese system. right now is obviously like they've got. their their massive property bubble that. they're that's looking really bad. They've got a population crisis. The. only way out for them is the AI stuff. right now. Like the really the only path. for them is that um which is why they're. they're working it so hard. But the the. the stories of just like billions and.

tens of billions of dollars being lit on. fire specifically in the semiconductor. industry on the in the AI industry. Like. that's a a drag force that they're. dealing with constantly that we don't. have here in the same way. So it's it's. the sort of like the the different um. structural advantages and weaknesses of. both systems. And when we think about. what do we need to do to to counter this. to to be active in this space to be a. live player again uh it means factoring. in like how do you Yeah. I mean how do. you take advantage of of some of those. opportunities that their system presents. that that ours doesn't? When you say be.

a live player again like where do you. position us? Um it's I think it remains. to be so right now this administration. is obviously taking bigger swings um. that what are they doing differently? Uh. so well I mean things like tariffs I. mean they're not shy about trying new. stuff and you know tariffs are are very. complex in the space like the impact the. actual impact of the tariffs and and not. universally good but the onoring effect. is also something that you really want. So it's a very mixed bag. Um, but this.

certainly an administration that's like. willing to do high stakes big moves in a. way that other administrations haven't. and in a time when you're looking at a. transformative technology that's going. to like upend so much about the way the. world works, you can't afford to have. that mentality we're just talking about. with like the nervous I mean you. encountered it with the staffers, you. know, in the when booking the podcast. with the presidential um cycle, right? like the kind of like nervous antsy. staffer who everything's got to be.

controlled and it's got to be like just. so yeah it's like if you like the like. you know wrestlers have that mentality. of like just like aggression like like. feed in right feed forward. Don't just. sit back and like wait to take the. punch. It's not like uh one of the guys. who who helped us out on this has this. saying. He's like um [ __ ] you, I go. first and it's always my turn. Right? That's what success looks like when you. actually are managing these kinds of. national security issues. The mentality. we had adopted was this like sort of.

siege mentality where we're just letting. stuff happen to us and we're not feeding. in. That's something that I'm much more. optimistic about in this context. Um. it's tough too because I understand. people who who hear that and go like. well look you're talking about like. escalator this is an escalatory agenda. Again I actually think paradoxically. it's not. It's about keeping adversaries. in check and training them to respect. American territorial integrity, American. technological sovereignty. Like you you. don't get that for free. And if you just.

sit back, you're that is escalatory. It's just Yeah. And basically the the. sub threshold version of like, you know, like the World War II appeasement thing. where back, you know, Hitler was like. was was taking uh uh he was taking. taking Austria, he was remilitarizing. [ __ ] He was doing this, he was doing. that. And the British were like, "Uh, okay. We're going to let him just take. one more thing and then he will be. satisfied." And that just maybe I have.

the little bit of Poland, please. A. little bit of Poland. Maybe the. Czechoslovakia is looking awfully fine. And so this is basically like they fell. into that pit like that tar pit back in. the day because they're, you know, peace. in our time. Yeah. The peace in our. time, right? and um and and to some. extent like we we've we've still kind of. learned the lesson of not letting that. happen with territorial boundaries, but. that's big and it's visible and happens. on the map and you can't hide it. Whereas one of the risks, especially. with the previous administration, was.

like um there's these like sub threshold. things that don't show up in the news. and that are they're they're calculated. like that basically um our adversaries. know because they know history. They. know not to give us a Pearl Harbor. They. know not to give us a 9/11 because. historically countries that give America. a Pearl Harbor end up having a pretty. bad time about it. And so why would they. give us a reason to come and bind. together against an obvious external.

like threat or risk when they can just. like keep chipping away at it? And this. is one of the things like we have to. actually elevate that and realize this. is what's happening. this is the. strategy. We need to we need to take. that like let's not do appeasement. mentality and push it across in these. other domains because that's where the. real competition is going on. That's. where it gets so fascinating in regards. to social media because it's imperative. that you have an ability to express. yourself. It's like it's very valuable. for everybody. The free exchange of.

information, finding out things that are. you're not going to get from mainstream. media and it's led to the rise of. independent journalism. It's all great, but also you're being manipulated like. left and right constantly, and most. people don't have the time to filter. through it and try to get some sort of. objective sense of what's actually going. on. It's true. It's like our our free. speech, it's like it's the layer where. our society figures stuff out. And when. ad if adversaries get into that layer, they're like almost inside of our of our.

of our brain. And there's ways of. addressing this. Like one of the. challenges obviously is like um so you. know they they try they try to push an. extreme opinions in in either direction. and it's that part is actually it's it's. kind of difficult because while um the. the most extreme opinions are like are. also the most likely generally to be. wrong. They're also the most valuable. when they're right because they tell us. a thing that we didn't expect by. definition that's true and that can.

really advance us forward. And so I mean. the the there there are actually. solutions to this. I mean this this. particular thing is isn't an area we. we're we're like too immersed in but one. of the solutions that um has been. bandied about is like you know like um. you might know like poly market. prediction markets and stuff like that. um where uh at least you know. hypothetically if you have a prediction. market around like if we do this policy. this thing will will or won't happen.

that actually creates a challenge around. trying to manipulate that view or that. market because what ends up happening is. like if you're an adversary and you want. to not just like manipulate a. conversation that's happening in social. media which is cheap but manipulate a. prediction the price on a prediction. market. You have to buy in you have to. spend real resources and if you're to. the extent you're wrong and you're. trying to create a wrong opinion you're. going to lose your resource. So you. actually you actually can't push too far.

too many times or you will just get your. money taken away from you. So I think. like that's that's one approach where. just in terms of preserving discourse um. some of the stuff that's happening in. prediction markets is actually really. interesting and really exciting even in. the context of bots and and AIS and. stuff like that. This is the one way to. find truth in the system is find out. where people are making money. Exactly. Put your money where your mouth is. Right. proof of work. Like this is that.

that is what just like the market is. theoretically too, right? It's got. obviously big big issues, but and can be. manipulated in the short term, but in. the long run like this is one of the. really interesting things about startups. too like when you when you run into. people in the early days um by. definition their startup looks like it's. not going to succeed, right? That is. what it means to be a seedstage startup, right? If it was obvious you were going. to succeed, you would you know the. people would have you would have raised. more money already. Yeah. So what you. end up having is like these highly. contrarian people who like despite.

everybody telling them that they're. going to fail just believe in what. they're doing and think they're going to. succeed. And that's I I think that's. part of what really like kind of shapes. the the the startup founder soul in a. way that's really constructive. It's. it's also something that if you look at. the Chinese system is very different. You you raise money in very different. ways. You're coupled to the state. apparatus. Like you're both dependent on. it and you're you're supported by it. But there there's there's just like a. lot of different ways and it makes it. hard for Americans to relate to Chinese. and vice versa and understand each.

other's systems. One of the biggest. risks as you're like thinking through. what is your posture going to be. relative to these countries is you fall. into thinking that their their. traditions, their way of thinking about. the world is the same as your own. And. that's that's something that's been an. issue for us with China for a long time. is, you know, hey, they'll liberalize, right? Like bring them into the World. Trade Organization. It's like, "Oh, well, actually, they're they're they'll. sign the document, but they won't they. won't actually like live up to any of. the commitments." And it's it makes. appeasement really tempting because. you're thinking, "Oh, they're just like. us." Like, they're just around the. corner. They're we're going to like if.

we just like reach out the old branch a. little bit further, they're going to. they're going to come around. It's like. a guy who's stuck in the friend zone. with a girl. Like, one day she's going. to come around and realize I'm a great. catch. You keep on trucking, buddy. One day, China's going to be my bestie. We're. going to be besties. He just We just. need an administration that reaches out. to them and just lets them know, man. There's no reason we should be. adversaries. We're all just people on. planet Earth together. I mean, like. Yeah. I together we like I I honestly.

wish that was true. So amazing. Maybe. that's what AI brings about. Maybe AI, maybe super intelligence realizes, "Hey, you [ __ ] apes, you territorial apes. with thermonuclear weapons." How about. you shut the [ __ ] up? You guys are doing. the dumbest thing of all time, and. you're being manipulated by a small. group of people that are profiting in. insane ways off of your misery. So, let's just cut the [ __ ] and figure out a.

way to actually equitably share. resources because that's the big thing. You're all stealing from the earth, but. some people stole first and those people. are now controlling all the [ __ ]. money. How about we stop that? Wow. We. just we covered a lot of ground there. Well, that's what I would do if I was to. if I was super. intelligent, stopped all that. That. actually is like So, this is not like. relevant to the risk stuff or to the. whatever at all, but it's just. interesting. So there's there's actually. theories like in the same way that. there's theories around um power.

seeeking and stuff around around super. intelligence there's theories around. like how super intelligences do deals. with each other right and you actually. like you have this intuition that which. is exactly right which is that hey two. super intelligences like actual legit. super intelligences should never. actually like fight each other. destructively in the real world right. like that seems weird that shouldn't. happen because they're they're so smart. and in fact like there's theories. around. They can they can kind of do. perfect deals with each other based on.

like if we're two super intelligences, I. can kind of assess like how powerful you. are, you can assess how powerful I am. and we can we can actually like um we. can actually decide like well uh well if. we did fight a war against each other. like you would have this chance of. winning I would have that chance of. winning and so let's it would assess. instantaneously that there's no benefit. in that and also it would know something. that we all know which is the rising. tide lifts all boats. But the problem is. the people that already have yachts,

they don't give a [ __ ] about your boat. Like, hey, hey, hey, that water's mine. In fact, you shouldn't even have water. Well, hopefully it's so positive some, right, that even they en enjoy the. benefits. But, but I mean, you're right. This is the issue right now. And one of. the the like the nice things, too, is as. you as you build up your your ratchet of. AI capabilities, it does start to open. some opportunities for actual like trust. but verify, right? Which is something. that we can't do right now. It's not. like with nuclear stockpiles where we've. had some success in some some context. with like enforcing you know treaties. and stuff like that sending inspectors.

in and all that. Um with with AI right. now like how can you actually prove that. like some international agreement on the. use of AI is being observed even if we. figure out how to control these systems. How can we make sure that you know China. is baking in those that those control. mechanisms into their training runs and. that we are and how can we prove it to. each other without having total access. to the compute stack. We don't really. have a solution for that. There are all. kinds of programs like this like Flex. Heg thing. But anyway, th those are not.

going to be online by like 2027. And so. one hope but it's really good that. people are working on them cuz like you. want to you want to like you want to be. positioned for catastrophic success. Like what if something great happens and. like or we have more time or or. whatever. you want to be working on this. stuff that that allows this kind of this. kind of control or oversight that that's. kind of handsoff where you know in. theory you can you can give you can hand. over GPUs to an adversary inside this. like box with these encryption things.

Um the people we've spoken to in in. these in the spaces that actually try to. like break into boxes like this are like. well that probably not going to work but. who knows it might. Yeah. So the hope is. that as you build up your AI. capabilities, basically it starts to. create solutions. So it starts to create. ways for, you know, two countries to. verifiably adhere to some kind of. international agreement or to find, like. you said, like paths for deescalation. That's the sort of thing that that we. actually could get to. And that's one of. the the strong positives of where you.

could end up going. That would be what's. really fascinating. Artificial general. intelligence becomes super intelligence. and it immediately weeds out all the. corruption. goes, "Hey, this is the. problem." Like a massive doge in the. sky. Exactly. Like, we figured it out. You guys are all criminals and expose it. to all the people like these people that. are your leaders have been profiting and. they do it on purpose and this is how. they're doing it and this is how they're. manipulating you and these are all the. lies that they've told. I'm sure that. list is pretty Whoa. It almost be scary.

Like if you could x-ray the world right. now and like see all the You'd want an. MRI. You want to get like down to the. tissue. Yeah, you're right. probably. Yeah, you want to get down to the. cellular level, but like it it would be. offshore accounts. Then you'd start. find there would be so much like the. stuff that comes out, you know, from. just randomly, right? Just just random. [ __ ] that comes out like Yeah. the the. um I forget that that that like.

Argentinian I think you what you were. talking about like but the the. Argentinian thing that that uh came out. a few years ago around all the oligarchs. and the Street thing. Yeah, the Mer. Street. Yeah, the um laundromat there. The the laundromat movie. You ever seen. that? Panama Papers. The Panama Papers. I never saw that. No. Good movie. Is it. called the Panama Papers? The movie? It's called The Laundromat. Yeah. Okay. You you remember the Panama Papers? Do. you know? Roughly. Yeah. It's like all. the all the oligarchs like stashing. their uh stashing their their cash like.

offshore tax haven stuff. Yeah. It's. like and uh and like some lawyer b or. some someone basically blew it wide open. and so you got to see like every every. like oligarch and rich person's like you. like financial [ __ ] like every once in a. while right the world gets just like a. flash of like oh here's what's going on. under the surface it's like oh [ __ ] and. then we all like go back to sleep what. what's fascinating is like the. unhidables right the the little things. that can't help but give away what is.

what is happening like you think about. this in AI quite a bit. Um, you know, some things that are hard for companies. to hide is like they'll have a a job. posting that they'll put they they've. got to advertise to to recruit. So, you'll see like, oh, interesting. Like, oh, OpenAI is looking to hire some. people from hedge funds. Um, hm. Like, I. wonder what that means. I wonder what. that implies. Like, if you think about. all of the leaders in kind of the AI. space, think about the Medallion Fund. for example, right? This is like super. successful hedge fund. Uh, very f like. what? The man who broke the the man who.

broke the market. Man who broke the. market. the famous book about the the. founder of the Medallion Fund and like. this is basically like a fund that they. make like ridiculous like $5 billion. returns every year kind of guaranteed. So so much so they have to cap how much. they invest in the market because they. would otherwise like move the market too. much like affect it. And the [ __ ] up. thing about like the way they trade and. this is so this is like 20-year-old. information but it's still indicative. because like you can't get current. information about their strategies but.

one of the things that they were the. first to kind of go for and figure out. is they were like um okay our they. basically were the first to kind of. build what was at the time as much as. possible an AI that autonomously did. trading at at at like great speeds and. that had like no human oversight and. just worked on its zone. And what what. they found was the strategies that were. the most. successful were the ones that humans. understood the least. Because if you.

have a strategy that a human can. understand, some human's going to go and. figure out that strategy and trade. against you. Whereas, if you have the. kind of the balls to go like, "Oh, this. thing is doing some weird [ __ ] that I. cannot understand no matter how hard I. try. let's just [ __ ] yolo and trust. it and like and make it work. If you. have all the stuff debugged and if you. have the whole if the whole system is. working right, that's where your biggest. successes are. So, what kind of. strategies are you talking about? Oh, I. I mean like I so I I don't I don't know.

specific analogy maybe this will this. like so how are how are AI systems. trained today, right? Oh, so just as a. trading strategy, sorry, I'll just So, yeah, you buy like some as an example. like you buy like you buy this stock um. uh th the Thursday after the full moon. and then sell it like the Friday after. the new moon or some like random [ __ ]. like that that it's like why does that. even work? Like why would why would that. even work? So, so to to like to to sort.

of um explain why these these um. strategies work better, if if you think. about how AI systems are trained today, um you basically very roughly you start. with this blob of numbers that's called. a a model and you feed it input, you get. an output. If the output you get is no. good, if you don't like the output, you. basically [ __ ] around with all those. numbers, change them a little bit, and. then you try again. You're like, "Oh, okay, that's better." And you repeat. that process over and over and over with. different inputs and outputs. And.

eventually those numbers, that. mysterious ball of numbers starts to. behave well. It starts to make good. predictions or generate good outputs. Now, you don't know why that is. You you. just know that it does a good job at. least where you've tested it. Now, if. you slightly change what you tested on, suddenly you could discover, oh [ __ ]. it's catastrophically failing at that. thing. These things are very brittle in. that way. And that's part of the reason. why chat GPT will just like completely. go on a psycho bingefest every once in a. while if you give it a prompt that has.

like too many exclamation points and. asterises in it or something like these. these systems are weirdly weirdly. brittle in that way. But applied to. investment strategies if all you're. doing is saying like optimize for like. optimize for returns, give it give it. inputs, give it make me more money by. the end of the day. It's like an easy. goal. like it's a a very like clear-cut. goal, right, that you can give a. machine. So, you end up with a a machine. that gives you these very like it is a. very weird strategy. This ball of. numbers isn't human understandable. It's.

just really [ __ ] good at making. money. And why is it really [ __ ] good. at making money? I don't know. I mean, it just kind of does the thing. And I'm. making money, I don't ask too many. questions. That's kind of like the So, so when you try to impose on that system. human interpretability, you pay what in. the AI world is known as the. interpretability tax. Basically, you're. adding another constraint. And the. minute you start to do that, you're. forcing it to optimize for something. other than pure rewards. Like doctors. using AI to diagnose diseases are less. effective than the chatbot on its own.

That's actually related, right? That's. related. If you want if you want that. system to get good at diagnosis, that's. one thing. Okay, just [ __ ] make it. good at diagnosis. If you want it to be. good at diagnosis and to produce. explanations that a good doctor Yeah. will go like, okay, I'll use that. Well, great. But guess what? Now you're. spending some of that precious compute. on something other than just the thing. you're trying to optimize for. And so. now that's going to come at a cost of. the actual performance of the system. And so if you are going to optimize like. the [ __ ] out of making money, you're.

going to necessarily deoptimize the [ __ ]. out of anything else, including being. able to even understand what that system. is doing. And that's kind of like at the. heart of a lot of the kind of big. picture AI strategy stuff is people are. wondering like how much how much uh. interpretability tax am I willing to pay. here and how much does it cost and. everyone's willing to go a little bit. further and a little bit further and so. so open actually had a a paper where. they or I guess a blog post where they. talked about this and they were like. look um right now uh we have this uh.

this um uh essentially this like thought. stream that our model produces on the. way to generating its final output and. that thought stream like we don't want. to touch it to make it like. interpretable to make it make sense. because if we do that then essentially. it'll be optimized to convince us of. whatever the thing is that we want it to. do to behave well so it's like you've if. you've used like one of like an open AAI. model recently right like 03 or or. whatever it's it's doing its thinking.

before it starts like outputting the. answer and so that thinking is Yeah, we're supposed to like be able to read. that and kind of get it, but also we we. don't want to make it too legible. because if we make it too legible, it's. going to be optimized to be legible and. and to be convincing rather than to fool. us basically. I mean, yeah, exactly. Oh, Jesus Christ. But that that's so that's. the making me less comfortable than I. thought you would. Even Even after I.

knew coming Jamie and I were talking. about it before like how bad are they. going to freak us out? You're freaking. me out more. Well, I mean, okay, so I I. do want to highlight so so the game plan. right now on the positive end, let's see. how this works. Jesus. Uh Jamie, do you. feel the same way? Uh yeah. I mean, I I have articles I didn't bring. up that are supporting some of this. stuff. Like uh today, China quietly made. some chip that they shouldn't have been. able to do because of the sanctions that. it's basically based off of their just.

sheer will. Okay, so there's there. there's good news on that one at least. Um, this is a kind of a [ __ ]. strategy that they're using. So there. there's Okay, so when you make these. insane like five n Let's read the for. people just listening. China quietly. cracks five nanometer. That's without. EUV. What is EUV? Extreme ultraviolet. ultraviolet. Uh, how SMIC defied the. chip sanctions with sheer engineering. Yeah. So this is like um and espionage.

Uh so there there's but actually though. so so there's um a good reason that a. lot of these articles uh are uh making. it seem like this is a huge. breakthrough. It actually isn't as big. as it seems. Um so so okay if you want. to make really really really really. exquisite change look at this quote. Moore's law didn't die wrote it moved to. Shanghai. Instead of giving up, China's. grinding its way forward, layer by. layer, pixel by pixel. The future of.

chips may no longer be written by who. holds the best tools, but by who refuses. to stop building. The rules are changing. and DUV just lit the fuse. Boy. Yeah. So, so I mean, who wrote that article? You can you can Isismo China. There it. is. Yeah. You can view that as like. Chinese propaganda in a way, actually. So what what what's actually going on. here is um if so the Chinese only have. these deep ultraviolet lithography. machines that's like a lot of syllables. but it's just a glorified chip like it's. a giant laser that that zaps your chips.

to like make the chips when when you're. fabing them. So we're talking about like. you you do these atomic layer patterns. on the chips and [ __ ] and like what this. UV thing does is it like fires like a a. really high power laser beam laser beam. Yeah. They attach to the head of sharks. that just shoot at the chips. Sorry, that was like an Austin Powers. Anyway, they they'll like shoot it at the chips. and uh that causes depending on how the. the thing is is designed, they'll like. have a liquid layer of the stuff that's.

going to go on the chip. The UV is. really really tight and causes it. Exactly. causes it to harden and then. they wash off the liquid and they do it. all over again. Like basically this is. just imprinting a pattern on a chip. So. whatever tiny printer Yeah. So that's. it. And so the the exquisite machines. that we get to use or that they get to. use in Taiwan are called extreme. ultraviolet lithography uh machines. These are those crazy lasers. Um the. ones that China can use because we've. prevented them from getting any of those. extreme ultraviolet lithography.

machines. The ones China uses are. previous generation machines called deep. ultraviolet and they can't actually make. chips as high a resolution as ours. So. what they do is and and what this. article is about is they basically take. the same chip, they zap it once with DUV. and then they got to pass it through. again, zap it again to to to get closer. to the level of resolution we get in one. pass with our exquisite machine. Now the. problem with that is you got to pass the. same chip through multiple times which. slows down your whole process. It means.

your yields at the end of the day are. lower. It adds errors. Yeah. Yeah. Which. makes it more costly. We've known that. this is a thing. It's called. multiatterning. It's been a thing for a. long time. There's nothing new under the. sun here. China has been doing this for. a while. Um but uh so it's not actually. a huge shock that this is happening. The. question is always when you look at an. announcement like this, yields yields. yields. How like what percentage of the. chips coming out are actually usable and. how fast are they they coming out that. determines like is it actually. competitive? And that article too like.

this ties into the propaganda stuff we. were talking about, right? If you read. an article like that, you could be. forgiven for going like, "Oh man, our. expert controls like just aren't. working, so we might as well just give. them up." When in reality, because like you look at the source. like, and this is and this is how you. know that also this is like this is one. of their propaganda things is like you. look at Chinese news sources, what are. they saying? What are the beats that. that are like common? And you know, just. because of the way their media is set. up, totally different from us and we're.

not used to analyzing things this way. But when you read something in like. South China Morning Post or like the. Global Times or Shin Hua in a few. different places like this and it's the. same beats coming back, you know that. someone was handed a brief and it's like. you got to hit this point, this point, this point and yep, they're going to. find a way to work that into the news. cycle over there. Jeez. And it's also like slightly true like it. Yeah, they they did manage to make chips. at like five nanometers. Cool. It's not. a lie. It's just it's the same like. propaganda technique, right? You're not.

most of the time you're not going to. confabulate something out of nothing. Rather like you start with the truth and. then you push it just a little bit. Just. a little bit and you keep pushing. pushing pushing. Wow. How much is this. administration aware of all the things. that you're talking about? So So they're. actually. um they they've got some right now. they're they're in the middle of of like. staffing up some of the key positions. because it's a new administration still. and this is such a technical domain. Um. they've got people there who are like.

like at at the kind of working level. really sharp. They have some people now. um Yeah. in in places like especially in. some of the export control offices now. who are some of the best in the. business. Yeah. and and and that's. that's really important. Like this is a. it's a weird space because so when you. want to actually recruit for for you. know government roles in the space it's. really [ __ ] hard because you're. competing against like an open AI like. very like low range salaries like half a.

million dollars a year. The government. pay scale needless to say is like not. work. I mean Elon worked for free. He. can he can afford to but but still. taking a lot of time out of his his day. Um there's a lot of people like that who. are like, you know, they they can't. justify the cost. Like they can't afford. they literally can't afford to work for. the government. For the government. Why. would they? Yeah. Exactly. So whereas. China is like you don't have a choice, [ __ ] Yeah. Yeah. And that's what they. say. That's they the the Chinese word. for [ __ ] is really biting. Like if you. if you translated that, it would be a.

real real sting. I'm sure. It's kind of. crazy because it seems almost impossible. to compete with that. I mean, that's. like the perfect setup. If you wanted to. control everything and you wanted to. optimize everything for the state, that's the way you would do it. Yeah. But it's also easier to to make errors. and be wrong footed in that way. And. also the basically that system only. works if the dictator at the top is just. like very competent because the the the. risk always with a dictatorship is like,

oh, the dictator turns over and now it's. like just a total dumbass and now you're. the whole thing. I mean, look, we just talked about like. information echo chambers online and. stuff. The ultimate information echo. chamber is the one around Xiinping right. now because no one wants to give him bad. news. I'm I'm not going to I don't you. know like and and so and you have this. and and this is what you keep seeing, right? Is like with these um uh like um. like provincial level debt in in China, right? which is so awful is like people. trying to hide money under imaginary.

money under imaginary mattresses and. then hiding those mattresses under. bigger mattresses until eventually like. no one knows where the liability is and. that and then you get a massive property. bubble and any number of other bubbles. that are due to to pop any time, right? So, and the longer it goes on like the. the the more like stuff gets squirreled. away. Like there's there's actually like. a story from the Soviet Union that's. that always like gets me, which is so um. Stalin obviously like purged and killed. like a millions of people in the 1930s, right? So by the 1980s the ruling pullet.

bureau of the Soviet Union um obviously. like things have been different, generations had turned over and all this. stuff, but those people the most. powerful people in the. USSR could not figure out what had. happened to their own families during. the purchase. Like the information was. just nowhere to be found because the the. machine of the state was just like so. aligned around like we just like we just.

got to kill as many [ __ ] people as we. can and like turn it over and then hide. the evidence of it and then kill the. people who killed the people and then. kill those people who killed those. people. It also wasn't just kill the. people, right? It was like in a lot of. like kind of gulag archipelago style it. it's it's about labor, right? because. the the fundamentals of the economy are. so [ __ ] that you basically have to find. a way to justify putting people in labor. camps and like That's right. But it but. it was very much like you grind mostly. or largely you grind them to death and. basically they they've gone away and you.

burn the records of of it happening. So. literally whole towns right that. disappeared like people who are like. there's no record or there's like or. usually the way you know about it is. there's like one dude. It's like this. one dude has a very precarious escape. story and it's like if if literally this. dude didn't get away, you wouldn't know. about the entire town that was like. wiped out. Yeah, it's crazy. Jesus. Christ. Yeah. The stuff that like apart. from that though, communism works really. well. Communism. Great. It just hasn't. been done right. That's right. I feel. like we could do it right. And we have a. 10-page plan. Uh that Yeah, we came real. close. We came real close. So close.

Yeah. Yeah. And that's what the blue no. matter who people don't really totally. understand. Like we're not even talking. about political parties, we're talking. about power structures. Yeah. And we. came close to a terrifying power. structure. And it was willing to just do. whatever it could to keep it rolling. And it was rolling for four years. It. was rolling for four years without. anyone at the helm. Show me the. incentives, right? I mean, that's that's. always the the question. Like yeah, one. of the things is too like when you have. such a big structure that's overseeing.

such complexity, right? Obviously, a lot. of stuff can hide in that structure and. it's it's actually kind it's it's not. unrelated to the whole AI picture like. the you need there's only so much. compute that you have at the top of that. system that you can spend right as the. president as a cabinet member like. whatever. Um you you can't look over. everyone's shoulder and like do their. homework. You can't do founder mode all. the way down in all the branches and all. the like action officers and all that. [ __ ] That's not going to happen.

5. seconds, but then the corrupt people who. run their own spend every day trying to. survive to like justify themselves. Yeah. Yeah. Well, that's the US aid. dilemma. Yeah. Yeah. As they're. uncovering, oh, this just insane amount. of NOS's like where's this going? We. talked about this the other day, but. India has an NGO for every 600 people. Wait, what? Yes, we need more NOS's. There's 3.3 million NOS.

in India. Do they do they like bucket. like what what are the categories that. they fall into? Like who [ __ ] knows? That's part of the problem that one of. the things that Elon had found is that. there's money that just goes out with no. receipts and it's billions of dollars. We need to take that further. We need an. NGO for every person in India. We will. get that eventually. It's the. exponential trend. We're going to it's. just like AI. the number of NOS's is is. doubling every year. We're making. incredible progress in [ __ ] It's.

the the NGO scaling law, the the. [ __ ] scaling law. Well, it's just. that unfortunately it's Republicans. doing it, right? So, it's unfortunately. the Democrats are going to oppose it. even if it's showing that there's like. insane waste of your tax dollars. I. thought some of the Doge stuff was. pretty. bipartisan like what like there's. congressional support at least on both. sides. No. Well, sort of. You know, I. think the real issue is in dismantling a. lot of these programs that you can point. to some good some of these programs do.

Yeah. The problem is like some of them. are so overwhelmed overwhelmed with. fraud and waste that it's like to keep. them active in the state they are like. what do you do? Do you rip the band-aid. off and start from scratch? Like what do. you do with the Department of Education? Do you say why are we number 39 when we. were number one? Like what did you guys. do with all that money? So the there's. problems there's this idea in software. engineering actually talking to one of. our employees about this which is like. refactoring right so when you're writing. like a bunch of software it gets really.

really big and hairy and complicated and. there's all kinds of like dumbass [ __ ]. and there's all kinds of waste that. happens in that in that codebase. There's this thing that you do every, you know, every like few months is you. do this thing called refactoring, which. is like you go like, okay, we have, you. know, 10 different things that are. trying to do the same thing. Let's get. rid of nine of those things and just. like rewrite it as the one thing. So. there's like a cleanup and refresh cycle. that has to happen whenever you're.

developing a big complex thing that does. a lot of stuff. The thing is like the. the US government at every level has. basically never done a refactoring of. itself. And so the the way that problems. get solved is you're like, well, we need. to do this new thing. So we're just. going to like stick on another appendage. to the beast and and get that appendage. to do that new thing. And like that's. been going on for 250 years. So we end.

up with like this beast that has a lot. of appendages, many of which do. incredibly duplicative and wasteful. stuff that if you were a software. engineer, just like not politically, just objectively looking at that as a. system, you'd go like, "Oh, this is a. catastrophe." And like we have processes. that the. indust what needs to be done to fix. that. You have to refactor. But they. haven't done that. Hence the $36. trillion of debt. It It's a problem too.

though in all like when you're a big. enough organization, you run into this. problem. Like Google has this problem. famously, Facebook like we have we have. friends like like Jason. So Jason's the. the guy you spoke to about that like um. so so he's he's like a startup engineer. So so he works in like relatively small. code bases and he he like you know can. hold the whole codebase in his head at a. time. But when you move over to um you. know Google to Facebook like all of a. sudden this gargantuan codebase starts. to look more like the complexity of the.

US government just like very you know. very roughly in terms of scale right so. now you're like okay well we want to add. functionality. um and but so we want to incentivize our. our teams to to build products that are. going to be valuable and the challenge. is the best way to incentivize that is. to to give people incentives to build. new functionality not not to refactor. there's no glory if you work at Google. there's glory in refactoring. If you. work at Meta, there's no glory in. refactoring. Like friends of ours, there's no promotion, right? There's no. Exactly. You have to be a product owner.

So, you have to like invent the next. Gmail. You got to invent the next Google. calendar. You got to do the next, you. know, Messenger app. That's how you get. promoted. And so, you've got like this. attitude. You you go into there and. you're just like, let me crank this. stuff out and like try to ignore all the. [ __ ] in the codebase. No glory in there. And what you're left with is this like a. this Frankenstein monster of a codebase. that you just keep stapling more [ __ ]. onto. And then b this massive graveyard. of apps that never get used. This is. like the thing Google is famous for. If.

you ever see like the Google graveyard. of apps, it's like all these things that. you're like, "Oh yeah, I guess I kind of. remember Google Meet." Somebody made. their career off of launching that [ __ ]. and then pieced out and it died. That's. that's like the incentive structure at. Google, unfortunately. And and it's it's also kind of the only. way to do I mean or maybe it's probably. not but um in the world where humans are. doing the oversight that's your. limitation right you got some people at. the top who have a limited bandwidth and. compute that they can dedicate to like. hunting down the problems AI agents. might actually solve that right you.

could like actually have the you know a. sort of autonomous AI agent that is the. autonomous CEO or something go into an. organization and uproot all the things. and do that refactor you could get way. more efficient organizations out of. that. I mean, like thinking about like. government corruption and waste and. fraud. That's the kind of thing where. those sorts of tools could be radically. empowering, but you got to, you know, you got to get them to work right and. and for you. You've given us a lot to think about. Is. there anything more? Should we wrap this.

up? If if we've made you sufficiently. uncomfortable. I am super uncomfortable. Was the very uneasy. Was the butt tap. too much at the beginning or was No, it. was fine. No, that was fine. All of it. was weird. It's uh it's just you know I always try. to look at some nonsynical way out of. this. Well the the thing is like there. are paths out. We talked about this and. the fact that a lot of these problems. are just us tripping on our own feet. So. if we can just like unfuck ourselves a. little bit. We are act we can unleash a.

lot of this stuff and as long as we. understand also the bar that security. has to hit and how important that is. like we actually can put all this stuff. together. We have the capacity. It it. all exists. It just needs to actually. get aligned and around around an. initiative and we have to be able to. reach out and touch. On the control side. there's also a world where and this is. actually like if you talk to the labs. this is what they're actually planning. to do. Um, but it's a question of how. methodically and carefully they can do. this. The plan is to ratchet up.

capabilities and then scale in other. words. And then as you do that, you. start to use your AI systems, your. increasingly clever and powerful AI. systems to do research on technical. control. So you basically build the next. generation of systems. You try to get. that generation of systems to help you. just inch forward a little bit more on. the capability side. It's very. precarious balance but it's something. that like at least isn't insane on the. face of it and fortunately I mean is the. the default path like or the labs are.

talking about the that kind of control. element as being a key pillar of their. but these conversations are not. happening in China. So what do you think. they're doing to keep AI from uprooting. their system? So so that's interesting. Uh there's because I would imagine they. don't want to lose control, right? There's a lot of ambiguity and. uncertainty about what's going on in. China. So there's been a lot of like. track 1.5 track 2 diplomacy basically. where you have you know non-government. guys from one side talk to government. guys from the other side talk to. non-government uh from the other side. and kind of start to align on like okay.

what do we think the issues are um you. know the the Chinese are there are a lot. of like freaked out Chinese researchers. and and who've come out publicly and. said hey like we're really concerned. about this whole loss of control thing. their public statements and all that you. also have to be mindful that any. statement the CCP puts out is a. statement they want you to see right so. when they say like, oh yeah, we're. really worried about this thing. It's. genuinely hard to assess what that even. means. Um, but there like as you as you. start to build these systems, we expect.

you're going to see some evidence of. this [ __ ] before and it's not. necessarily it's not like you're going. to build the system necessarily and have. it take over the world like what we see. with agents. Yeah. Yeah. So I was. actually gonna add to I think there's a. really really good point and um and. something where. like opensource AI is like even you know. could potentially have an effect here. Um so a lot of a couple of the major. labs like opening Ianthropic I think. came out recently and said like look um. we we're on the cusp our systems are on.

the cusp of being able to help a total. novice like someone with no experience. develop and deploy and release a known. biological threat. And that's like. that's something we're going to have to. grapple with over the next few months. And eventually like um capabilities like. this, not necessarily just biological, but also cyber and other areas are going. to come out in open source. And when. they come out in open source, basically. for anybody to download for anybody to. download and use. when they come out in. open source, like you might actually.

start to see some like some some things. happen like some some incidents like. some some major hacks that were just. done by like a random [ __ ] who. just wants to see the world burn, but. that wakes us up to like, oh [ __ ] these. things actually are powerful. I think. one of the aspects also here is um we're. still in that postcold war honeymoon, many of us, right? that mentality like. not everyone has like wrapped their. heads around this stuff and the like.

what needs to happen is something that. makes us go like oh damn we act like we. weren't even really trying this entire. time cuz this is like this is the the. 9/11 effect. This is the Pearl Harbor. effect. Once you have a thing that. aligns everyone around like oh [ __ ] this. is real and we actually need to do it. and we're freaked out. We're actually. safer. We're safer when we're all like, "Okay, something important needs to. happen." And Right. Instead of letting.

them just slowly chip away. Exactly. And. so we like we need to have some sort of. shock and we probably will get some kind. of shock like over the next few months. the way things are trending. And when. that happens then but I mean like it's. years that makes you feel that makes you. feel bad. But because but because you. have the potential for this open source. like it's probably going to be like a. survivable shock, right? but but still a. shock. And so let us actually realign. around like, okay, let's actually. [ __ ] solve some problems for real.

And so putting together the groundwork, right, is is what we're doing around. like let's let's pre-think a lot of this. stuff so that like if and when the shock. comes, we have a break glass plan. We. have a we have a plan. And the the loss. of control stuff is similar like you. So, one interesting thing that happens. with AI agents today is they'll like. they'll get any so so an AI agent will. take a complex task that you give it. like find me I don't know the like best. sneakers for me online some [ __ ] like. that and they'll break it down into a. series of substeps and then each of.

those steps it'll farm out to a version. of itself say to execute autonomously um. the more complex a task is the more of. those little substeps there are in it. and so you can have an AI agent that. nails like 99% of of those steps But if. it screws up just one, the whole thing. is a flop, right? And so if you think. about like the sort of like loss of. control scenarios that a lot of people. look at are autonomous replication, like. the the model gets access to the. internet, copies itself onto servers and.

all that stuff. Um, those are very. complex movements. If it screws up at. any point along the way, that's a tell. Like, oh [ __ ] something's happening. there. And you can start to think about. like, okay, well, what went wrong? we. get another do, we get another try and. we can kind of learn from our mistakes. So there is this sort of like this. picture, you know, one camp goes, oh. well, we're going to kind of make the. super intelligence in a vat and then. it's it explodes out and we lose control. over it. That doesn't necessarily seem.

like the default scenario right now. It. seems like what we're doing is scaling. these systems. Um we might unhobble them. with big capability jumps. Um, but it's. also there's a component of this that is. a continuous process that lets us kind. of get our arms around it in a more. staged way. That's another thing that I. think uh is in our favor that we didn't. expect before and um as as a field. basically and I think that's that's a. good thing like that helps you kind of. detect these breakout attempts and do. things about them. All right, I'm going. to bring this home. Um I'm freaked out.

so thank you. Thanks for trying to make. me feel better. I don't think you did, but I really appreciate you guys and I. appreciate your perspective because it's. very important and it's very. illuminating. You know, it really gives. you a sense of what's going on. And I. think one of the things that you said. that's really important is like it it. sucks that we need a 911 moment or a. Pearl Harbor moment to realize what's. happening so we all come together. But. hopefully slowly but surely through. conversations like this, people realize. what's actually happening. You need one.

of those moments like every generation. Like that's how you get contact with the. truth and it's like it's painful but. like the light's on the other side. Thank you. Thank you very much. Thank. you. Bye everybody. [Music]. [Applause]. [Music].